

@article{sauty_impact_2023,
	title = {Impact of sex and {APOE}-$\varepsilon$4 genotype on patterns of regional brain atrophy in {Alzheimer}'s disease and healthy aging},
	volume = {14},
	issn = {1664-2295},
	url = {https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2023.1161527/full},
	doi = {10.3389/fneur.2023.1161527},
	abstract = {Alzheimer's Disease (AD) is a heterogeneous disease that disproportionately affects women and people with the APOE-$\varepsilon$4 susceptibility gene...},
	language = {English},
	journal = {Frontiers in Neurology},
	author = {Sauty, Benoît and Durrleman, Stanley},
	month = jun,
	year = {2023}
}



@article{garcia2018dynamic,
  title={Dynamic patterns of cortical expansion during folding of the preterm human brain},
  author={Garcia, Kara E and Robinson, Emma C and Alexopoulos, Dimitrios and Dierker, Donna L and Glasser, Matthew F and Coalson, Timothy S and Ortinau, Cynthia M and Rueckert, Daniel and Taber, Larry A and Van Essen, David C and others},
  journal={Proceedings of the National Academy of Sciences},
  volume={115},
  number={12},
  pages={3156--3161},
  year={2018},
  publisher={National Academy of Sciences}
}

@article{fischl1999high,
  title={High-resolution intersubject averaging and a coordinate system for the cortical surface},
  author={Fischl, Bruce and Sereno, Martin I and Tootell, Roger BH and Dale, Anders M},
  journal={Human brain mapping},
  volume={8},
  number={4},
  pages={272--284},
  year={1999},
  publisher={Wiley Online Library}
}
@inproceedings{besenczi2024high,
  title={High performance groupwise cortical surface registration with multimodal surface matching},
  author={Besenczi, Renato and Guo, Yourong and Robinson, Emma C},
  booktitle={International Workshop on Biomedical Image Registration},
  pages={321--332},
  year={2024},
  organization={Springer}
}

@article{robinson2014msm,
  title={MSM: a new flexible framework for multimodal surface matching},
  author={Robinson, Emma C and Jbabdi, Saad and Glasser, Matthew F and Andersson, Jesper and Burgess, Gregory C and Harms, Michael P and Smith, Stephen M and Van Essen, David C and Jenkinson, Mark},
  journal={Neuroimage},
  volume={100},
  pages={414--426},
  year={2014},
  publisher={Elsevier}
}

@article{edwards2022developing,
  title={The developing human connectome project neonatal data release},
  author={Edwards, A David and Rueckert, Daniel and Smith, Stephen M and Abo Seada, Samy and Alansary, Amir and Almalbis, Jennifer and Allsop, Joanna and Andersson, Jesper and Arichi, Tomoki and Arulkumaran, Sophie and others},
  journal={Frontiers in neuroscience},
  volume={16},
  pages={886772},
  year={2022},
  publisher={Frontiers Media SA}
}

@article{montal2018cortical,
  title={Cortical microstructural changes along the Alzheimer's disease continuum},
  author={Montal, Victor and Vilaplana, Eduard and Alcolea, Daniel and Pegueroles, Jordi and Pasternak, Ofer and Gonz{\'a}lez-Ortiz, Sofia and Clarim{\'o}n, Jordi and Carmona-Iragui, Mar{\'\i}a and Ill{\'a}n-Gala, Ignacio and Morenas-Rodr{\'\i}guez, Estrella and others},
  journal={Alzheimer's \& Dementia},
  volume={14},
  number={3},
  pages={340--351},
  year={2018},
  publisher={Wiley Online Library}
}

@article{marquand2016understanding,
  title={Understanding heterogeneity in clinical cohorts using normative models: beyond case-control studies},
  author={Marquand, Andre F and Rezek, Iead and Buitelaar, Jan and Beckmann, Christian F},
  journal={Biological psychiatry},
  volume={80},
  number={7},
  pages={552--561},
  year={2016},
  publisher={Elsevier}
}


@article{i__lyu_8ed77aa9,
  author = {I. Lyu AND H. Kang AND N.D. Woodward AND M.A. Styner AND B.A. Landman},
  title = {Hierarchical spherical deformation for cortical surface registration},
  journal = {Carolina Digital Repository (University of North Carolina at Chapel Hill)},
  year = {2019},
  month = {01},
  doi = {10.17615/vwav-z318},
  issn = {},
  isbn = {},
  url = {https://doi.org/10.17615/vwav-z318},
  publisher = {University of North Carolina at Chapel Hill}
}
@article{mohamed_a__suliman_f3e96a79,
  author = {Mohamed A. Suliman AND Logan Z. J. Williams AND Abdulah Fawaz AND Emma C. Robinson},
  title = {Unsupervised Multimodal Surface Registration with Geometric Deep Learning},
  journal = {arXiv (Cornell University)},
  year = {2023},
  month = {01},
  doi = {10.48550/arxiv.2311.13022},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/2311.13022},
  publisher = {Cornell University}
}

@misc{fenqiang_zhao_124056ba,
  author = {Fenqiang Zhao AND Zhengwang Wu AND Gang Li},
  title = {Deep learning in cortical surface-based neuroimage analysis: a systematic review},
  journal = {Intelligent Medicine},
  volume = {3},
  issue = {1},
  pages = {46-58},
  year = {2022},
  month = {07},
  doi = {10.1016/j.imed.2022.06.002},
  issn = {2667-1026, 2096-9376},
  isbn = {},
  url = {https://doi.org/10.1016/j.imed.2022.06.002},
  publisher = {Elsevier BV}
}
@misc{nihms971379_pdf_3f63030e,
  author = {},
  title = {nihms971379.pdf},
  issn = {},
  isbn = {}
}
@article{jieyu_cheng_51d882c4,
  author = {Jieyu Cheng AND Adrian V. Dalca AND Bruce Fischl AND Lilla Zöllei},
  title = {Cortical surface registration using unsupervised learning},
  journal = {NeuroImage},
  volume = {221},
  pages = {117161-117161},
  year = {2020},
  month = {07},
  doi = {10.1016/j.neuroimage.2020.117161},
  pmid = {32702486},
  issn = {1053-8119, 1095-9572},
  isbn = {},
  url = {https://doi.org/10.1016/j.neuroimage.2020.117161},
  publisher = {Elsevier BV}
}
@article{jiale_cheng_c2b221f5,
  author = {Jiale Cheng AND Fenqiang Zhao AND Dan Hu AND Chao Cao AND Zhengwang Wu AND Xinrui Yuan AND Kangfu Han AND Lu Zhang AND Tianming Liu AND Dajiang Zhu AND Gang Li},
  title = {Unsupervised Cortical Surface Registration Network for Aligning Gyralnet},
  pages = {1-5},
  year = {2025},
  month = {04},
  doi = {10.1109/isbi60581.2025.10981138},
  issn = {},
  isbn = {},
  url = {https://doi.org/10.1109/isbi60581.2025.10981138}
}

@article{emma_c__robinson_385c243a,
  author = {Emma C. Robinson AND Kara Garcia AND Matthew F. Glasser AND Zhengdao Chen AND Timothy S. Coalson AND Antonios Makropoulos AND Jelena Božek AND Robert Wright AND Andreas Schuh AND Matthew Webster AND Jana Hutter AND Anthony N. Price AND Lucilio Cordero‐Grande AND Emer Hughes AND Nora Tusor AND Philip V. Bayly AND David C. Van Essen AND Stephen M. Smith AND A. David Edwards AND Joseph V. Hajnal AND Mark Jenkinson AND Ben Glocker AND Daniel Rueckert},
  title = {Multimodal surface matching with higher-order smoothness constraints},
  journal = {NeuroImage},
  volume = {167},
  pages = {453-465},
  year = {2017},
  month = {10},
  doi = {10.1016/j.neuroimage.2017.10.037},
  pmid = {29100940},
  issn = {1053-8119, 1095-9572},
  isbn = {},
  url = {https://doi.org/10.1016/j.neuroimage.2017.10.037},
  publisher = {Elsevier BV}
}
@article{i__lyu_8ed77aa9,
  author = {I. Lyu AND H. Kang AND N.D. Woodward AND M.A. Styner AND B.A. Landman},
  title = {Hierarchical spherical deformation for cortical surface registration},
  journal = {Carolina Digital Repository (University of North Carolina at Chapel Hill)},
  year = {2019},
  month = {01},
  doi = {10.17615/vwav-z318},
  issn = {},
  isbn = {},
  url = {https://doi.org/10.17615/vwav-z318},
  publisher = {University of North Carolina at Chapel Hill}
}

@online{ruoyu_zhang_f4f2965e,
  author = {Ruoyu Zhang AND Lihui Wang AND Kun Tang AND Jingwen Xu AND Hongjiang Wei},
  title = {GESH-NET: GRAPH-ENHANCED SPHERICAL HARMONIC CONVOLUTIONAL NETWORKS FOR CORTICAL SURFACE REGISTRATION},
  year = {2024},
  month = {10},
  issn = {},
  isbn = {}
}
@misc{fenqiang_zhao_124056ba,
  author = {Fenqiang Zhao AND Zhengwang Wu AND Gang Li},
  title = {Deep learning in cortical surface-based neuroimage analysis: a systematic review},
  journal = {Intelligent Medicine},
  volume = {3},
  issue = {1},
  pages = {46-58},
  year = {2022},
  month = {07},
  doi = {10.1016/j.imed.2022.06.002},
  issn = {2667-1026, 2096-9376},
  isbn = {},
  url = {https://doi.org/10.1016/j.imed.2022.06.002},
  publisher = {Elsevier BV}
}
@misc{nihms971379_pdf_3f63030e,
  author = {},
  title = {nihms971379.pdf},
  issn = {},
  isbn = {}
}
@article{jieyu_cheng_51d882c4,
  author = {Jieyu Cheng AND Adrian V. Dalca AND Bruce Fischl AND Lilla Zöllei},
  title = {Cortical surface registration using unsupervised learning},
  journal = {NeuroImage},
  volume = {221},
  pages = {117161-117161},
  year = {2020},
  month = {07},
  doi = {10.1016/j.neuroimage.2020.117161},
  pmid = {32702486},
  issn = {1053-8119, 1095-9572},
  isbn = {},
  url = {https://doi.org/10.1016/j.neuroimage.2020.117161},
  publisher = {Elsevier BV}
}
@article{jiale_cheng_c2b221f5,
  author = {Jiale Cheng AND Fenqiang Zhao AND Dan Hu AND Chao Cao AND Zhengwang Wu AND Xinrui Yuan AND Kangfu Han AND Lu Zhang AND Tianming Liu AND Dajiang Zhu AND Gang Li},
  title = {Unsupervised Cortical Surface Registration Network for Aligning Gyralnet},
  pages = {1-5},
  year = {2025},
  month = {04},
  doi = {10.1109/isbi60581.2025.10981138},
  issn = {},
  isbn = {},
  url = {https://doi.org/10.1109/isbi60581.2025.10981138}
}

@article{mariana_da_silva_63580395,
  author = {Mariana da Silva AND Kaili Liang AND Kara Garcia AND M. Jorge Cardoso AND Emma C. Robinson},
  title = {Differential patterns of cortical expansion in fetal and preterm brain development},
  journal = {bioRxiv (Cold Spring Harbor Laboratory)},
  year = {2025},
  month = {05},
  doi = {10.1101/2025.05.19.653958},
  pmid = {40475433},
  issn = {},
  isbn = {},
  url = {https://doi.org/10.1101/2025.05.19.653958},
  publisher = {Cold Spring Harbor Laboratory}
}
@article{deanne_k__thompson_6e77c25a,
  author = {Deanne K. Thompson AND Lillian G. Matthews AND Bonnie Alexander AND Katherine J. Lee AND Claire E. Kelly AND Chris Adamson AND Rod W. Hunt AND Jeanie L.Y. Cheong AND Megan Spencer‐Smith AND Jeffrey J. Neil AND Marc L. Seal AND Terrie E. Inder AND Lex W. Doyle AND Peter J. Anderson},
  title = {Tracking regional brain growth up to age 13 in children born term and very preterm},
  journal = {Nature Communications},
  volume = {11},
  issue = {1},
  year = {2020},
  month = {02},
  doi = {10.1038/s41467-020-14334-9},
  pmid = {32019924},
  issn = {2041-1723},
  isbn = {},
  url = {https://doi.org/10.1038/s41467-020-14334-9},
  publisher = {Nature Portfolio}
}

@article{ying_huang_38f9dd03,
  author = {Ying Huang AND Zhengwang Wu AND Fan Wang AND Dan Hu AND Tengfei Li AND Lei Guo AND Li Wang AND Weili Lin AND Gang Li},
  title = {Mapping developmental regionalization and patterns of cortical surface area from 29 post-menstrual weeks to 2 years of age},
  journal = {Proceedings of the National Academy of Sciences},
  volume = {119},
  issue = {33},
  year = {2022},
  month = {08},
  doi = {10.1073/pnas.2121748119},
  pmid = {35939665},
  issn = {0027-8424, 1091-6490},
  isbn = {},
  url = {https://doi.org/10.1073/pnas.2121748119},
  publisher = {National Academy of Sciences}
}
@article{heather_c__hazlett_e765bb26,
  author = {Heather C. Hazlett AND Hongbin Gu AND Brent C. Munsell AND Sun Hyung Kim AND Martin Styner AND Jason J. Wolff AND Jed T. Elison AND Meghan R. Swanson AND Hongtu Zhu AND Kelly N. Botteron AND D. Louis Collins AND John N. Constantino AND Stephen R. Dager AND Annette Estes AND Alan C. Evans AND Vladimir Fonov AND Guido Gerig AND Penelope Kostopoulos AND Robert C. McKinstry AND Juhi Pandey AND Sarah Paterson AND John R. Pruett AND Robert T. Schultz AND Dennis Shaw AND Lonnie Zwaigenbaum AND Joseph Piven},
  title = {Early brain development in infants at high risk for autism spectrum disorder},
  journal = {UNC Libraries},
  year = {2020},
  month = {04},
  doi = {10.17615/kpgb-9y78},
  issn = {},
  isbn = {},
  url = {https://doi.org/10.17615/kpgb-9y78}
}
@article{fan_wang_34eff68b,
  author = {Fan Wang AND Chunfeng Lian AND Zhengwang Wu AND Han Zhang AND Tengfei Li AND Yu Meng AND Li Wang AND Weili Lin AND Dinggang Shen AND Gang Li},
  title = {Developmental topography of cortical thickness during infancy},
  journal = {Proceedings of the National Academy of Sciences},
  volume = {116},
  issue = {32},
  pages = {15855-15860},
  year = {2019},
  month = {07},
  doi = {10.1073/pnas.1821523116},
  pmid = {31332010},
  issn = {0027-8424, 1091-6490},
  isbn = {},
  url = {https://doi.org/10.1073/pnas.1821523116},
  publisher = {National Academy of Sciences}
}
@article{lei_du_a58694ec,
  author = {Lei Du AND Kefei Liu AND Lei Zhu AND Xiaohui Yao AND Shannon L. Risacher AND Lei Guo AND Andrew J. Saykin AND Li Shen},
  title = {Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort},
  journal = {Bioinformatics},
  volume = {35},
  issue = {14},
  year = {2019},
  month = {05},
  doi = {10.1093/bioinformatics/btz320},
  pmid = {31510645},
  issn = {1367-4803, 1367-4811},
  isbn = {},
  url = {https://doi.org/10.1093/bioinformatics/btz320},
  publisher = {Oxford University Press}
}
@article{daniel_ferreira_d2b6ecab,
  author = {Daniel Ferreira AND C. Verhagen AND Juan Hernández AND Lena Cavallin AND Chunjie Guo AND Urban Ekman AND J‐Sebastian Muehlboeck AND Andrew Simmons AND José Barroso AND Lars‐Olof Wahlund AND Eric Westman},
  title = {Distinct subtypes of Alzheimer’s disease based on patterns of brain atrophy: longitudinal trajectories and clinical applications},
  journal = {Scientific Reports},
  volume = {7},
  issue = {1},
  pages = {46263-46263},
  year = {2017},
  month = {04},
  doi = {10.1038/srep46263},
  pmid = {28417965},
  issn = {2045-2322},
  isbn = {},
  url = {https://doi.org/10.1038/srep46263},
  publisher = {Nature Portfolio}
}
@article{cassidy_m__fiford_c8943d6a,
  author = {Cassidy M. Fiford AND Gerard R. Ridgway AND David M. Cash AND Marc Modat AND Jennifer M. Nicholas AND Emily N. Manning AND Ian B. Malone AND Geert Jan Biessels AND Sébastien Ourselin AND Owen Carmichael AND M. Jorge Cardoso AND Josephine Barnes},
  title = {Patterns of progressive atrophy vary with age in Alzheimer's disease patients},
  journal = {Neurobiology of Aging},
  volume = {63},
  pages = {22-32},
  year = {2017},
  month = {11},
  doi = {10.1016/j.neurobiolaging.2017.11.002},
  pmid = {29220823},
  issn = {0197-4580, 1558-1497},
  isbn = {},
  url = {https://doi.org/10.1016/j.neurobiolaging.2017.11.002},
  publisher = {Elsevier BV}
}
@article{l_a_chauveau_3d98be79,
  author = {Léa Chauveau AND Elizabeth Kuhn AND Cassandre Palix AND Francesca Felisatti AND Valentin Ourry AND Vincent de La Sayette AND Gaël Chételat AND Robin de Florès},
  title = {Medial Temporal Lobe Subregional Atrophy in Aging and Alzheimer's Disease: A Longitudinal Study},
  journal = {Frontiers in Aging Neuroscience},
  volume = {13},
  pages = {750154-750154},
  year = {2021},
  month = {10},
  doi = {10.3389/fnagi.2021.750154},
  pmid = {34720998},
  issn = {1663-4365},
  isbn = {},
  url = {https://doi.org/10.3389/fnagi.2021.750154},
  publisher = {Frontiers Media}
}


@article{christian_wachinger_655a2b71,
  author = {Christian Wachinger AND Polina Golland AND William S. Kremen AND Bruce Fischl AND Martin Reuter},
  title = {BrainPrint: A discriminative characterization of brain morphology},
  journal = {NeuroImage},
  volume = {109},
  pages = {232-248},
  year = {2015},
  month = {01},
  doi = {10.1016/j.neuroimage.2015.01.032},
  pmid = {25613439},
  issn = {1053-8119, 1095-9572},
  isbn = {},
  url = {https://doi.org/10.1016/j.neuroimage.2015.01.032},
  publisher = {Elsevier BV}
}
@article{poorya_chavoshnejad_49da10fe,
  author = {Poorya Chavoshnejad AND Liam Vallejo AND Songyao Zhang AND Yanchen Guo AND Weiying Dai AND Tuo Zhang AND Mir Jalil Razavi},
  title = {Mechanical hierarchy in the formation and modulation of cortical folding patterns},
  abstract = {The important mechanical parameters and their hierarchy in the growth and folding of the human brain have not been thoroughly understood. In this study, we developed a multiscale mechanical model to investigate how the interplay between initial geometrical undulations, differential tangential growth in the cortical plate, and axonal connectivity form and regulate the folding patterns of the human brain in a hierarchical order. To do so, different growth scenarios with bilayer spherical models that features initial undulations on the cortex and uniform or heterogeneous distribution of axonal fibers in the white matter were developed, statistically analyzed, and validated by the imaging observations. The results showed that the differential tangential growth is the inducer of cortical folding, and in a hierarchal order, high-amplitude initial undulations on the surface and axonal fibers in the substrate regulate the folding patterns and determine the location of gyri and sulci. The locations with dense axonal fibers after folding settle in gyri rather than sulci. The statistical results also indicated that there is a strong correlation between the location of positive (outward) and negative (inward) initial undulations and the locations of gyri and sulci after folding, respectively. In addition, the locations of 3-hinge gyral folds are strongly correlated with the initial positive undulations and locations of dense axonal fibers. As another finding, it was revealed that there is a correlation between the density of axonal fibers and local gyrification index, which has been observed in imaging studies but not yet fundamentally explained. This study is the first step in understanding the linkage between abnormal gyrification (surface morphology) and disruption in connectivity that has been observed in some brain disorders such as Autism Spectrum Disorder. Moreover, the findings of the study directly contribute to the concept of the regularity and variability of folding patterns in individual human brains.},
  journal = {Scientific Reports},
  volume = {13},
  issue = {1},
  year = {2023},
  month = {08},
  doi = {10.1038/s41598-023-40086-9},
  pmid = {37580340},
  issn = {2045-2322},
  isbn = {},
  url = {https://doi.org/10.1038/s41598-023-40086-9},
  publisher = {Nature Portfolio}
}
@article{t__tallinen_34f18a3c,
  author = {T. Tallinen AND Jun Young Chung AND François Rousseau AND Nadine Girard AND Julien Lefèvre AND L. Mahadevan},
  title = {On the growth and form of cortical convolutions},
  journal = {Nature Physics},
  volume = {12},
  issue = {6},
  pages = {588-593},
  year = {2016},
  month = {01},
  doi = {10.1038/nphys3632},
  issn = {1745-2473, 1745-2481},
  isbn = {},
  url = {https://doi.org/10.1038/nphys3632},
  publisher = {Nature Portfolio}
}
@article{nagehan_demirci_1aac96fd,
  author = {Nagehan Demirci AND Fatemeh Jafarabadi AND Xincheng Wang AND Shuolun Wang AND Maria A. Holland},
  title = {Consistency and variation in the placement of cortical folds: A perspective},
  abstract = {Cortical folds, known as gyri and sulci, are prominent features of the human brain that play a crucial role in its function. These folds exhibit both consistency and variation within and across individuals and species, presenting a scientific challenge to our understanding of the underlying mechanisms. In this perspective paper, we summarize current knowledge about fold development and placement. We discuss the temporal and anatomical differences between primary, secondary, and tertiary folds, highlighting the consistency of primary folds and the increasing variation in later-developing folds. We explore the biological and mechanical factors that influence fold placement, including gene expression, tissue growth, axonal tension, curvature, thickness, and stiffness, which likely work together in a complex, coupled manner. We also highlight the need for advanced computational modeling approaches to unravel the mechanisms of precise placement of primary folds and further our understanding of brain complexity. Statement of significance: Understanding the factors driving both the consistency and variation in fold patterns is essential for unraveling the functional implications and potential links to neurological and psychiatric disorders. Ultimately, gaining deeper insights into fold development and placement could have significant implications for our fundamental understanding of the brain, as well as mental health research and clinical applications.},
  journal = {Brain Multiphysics},
  volume = {5},
  pages = {100080-100080},
  year = {2023},
  month = {08},
  doi = {10.1016/j.brain.2023.100080},
  issn = {2666-5220},
  isbn = {},
  url = {https://doi.org/10.1016/j.brain.2023.100080},
  publisher = {Elsevier BV}
}
@misc{kiho_im_aac64736,
  author = {Kiho Im AND P. Ellen Grant},
  title = {Sulcal pits and patterns in developing human brains},
  journal = {NeuroImage},
  volume = {185},
  pages = {881-890},
  year = {2018},
  month = {03},
  doi = {10.1016/j.neuroimage.2018.03.057},
  pmid = {29601953},
  issn = {1053-8119, 1095-9572},
  isbn = {},
  url = {https://doi.org/10.1016/j.neuroimage.2018.03.057},
  publisher = {Elsevier BV}
}
@article{jessica_dubois_4fd34c18,
  author = {Jessica Dubois AND M. Benders AND Cristina Borradori-Tolsa AND Arnaud Cachia AND François Lazeyras AND Russia Hà-Vinh Leuchter AND Stéphane Sizonenko AND Simon K. Warfield AND Jean‐François Mangin AND Petra S. Hüppi},
  title = {Primary cortical folding in the human newborn: an early marker of later functional development},
  abstract = {In the human brain, the morphology of cortical gyri and sulci is complex and variable among individuals, and it may reflect pathological functioning with specific abnormalities observed in certain developmental and neuropsychiatric disorders. Since cortical folding occurs early during brain development, these structural abnormalities might be present long before the appearance of functional symptoms. So far, the precise mechanisms responsible for such alteration in the convolution pattern during intra-uterine or post-natal development are still poorly understood. Here we compared anatomical and functional brain development in vivo among 45 premature newborns who experienced different intra-uterine environments: 22 normal singletons, 12 twins and 11 newborns with intrauterine growth restriction (IUGR). Using magnetic resonance imaging (MRI) and dedicated post-processing tools, we investigated early disturbances in cortical formation at birth, over the developmental period critical for the emergence of convolutions (26–36 weeks of gestational age), and defined early 'endophenotypes' of sulcal development. We demonstrated that twins have a delayed but harmonious maturation, with reduced surface and sulcation index compared to singletons, whereas the gyrification of IUGR newborns is discordant to the normal developmental trajectory, with a more pronounced reduction of surface in relation to the sulcation index compared to normal newborns. Furthermore, we showed that these structural measurements of the brain at birth are predictors of infants' outcome at term equivalent age, for MRI-based cerebral volumes and neurobehavioural development evaluated with the assessment of preterm infant's behaviour (APIB).},
  journal = {Brain},
  volume = {131},
  issue = {8},
  pages = {2028-2041},
  year = {2008},
  month = {06},
  doi = {10.1093/brain/awn137},
  pmid = {18587151},
  issn = {0006-8950, 1460-2156},
  isbn = {},
  url = {https://doi.org/10.1093/brain/awn137},
  publisher = {Oxford University Press}
}
@article{cristina_llinares_ac65e99f,
  author = {Cristina Llinares AND Vı́ctor Borrell},
  title = {Deconstructing cortical folding: genetic, cellular and mechanical determinants},
  journal = {Nature reviews. Neuroscience},
  volume = {20},
  issue = {3},
  pages = {161-176},
  year = {2019},
  month = {01},
  doi = {10.1038/s41583-018-0112-2},
  pmid = {30610227},
  issn = {1471-003X, 1471-0048},
  isbn = {},
  url = {https://doi.org/10.1038/s41583-018-0112-2},
  publisher = {Nature Portfolio}
}
@article{byoung_il_bae_b38b0276,
  author = {Byoung-Il Bae AND Ian Tietjen AND Kutay Deniz Atabay AND Gilad D. Evrony AND Matthew B. Johnson AND Ebenezer Aquisman Asare AND Peter P. Wang AND Ayako Murayama AND Kiho Im AND Steven Lisgo AND Lynne M. Overman AND Nenad Šestan AND Bernard S. Chang AND A. James Barkovich AND P. Ellen Grant AND Meral Topçu AND Jeffrey Politsky AND Hideyuki Okano AND Xianhua Piao AND Christopher A. Walsh},
  title = {Evolutionarily Dynamic Alternative Splicing of GPR56 Regulates Regional Cerebral Cortical Patterning},
  abstract = {Development of surface folds of the human brain is controlled in sections. [Also see Perspective by Rash and Rakic ]},
  journal = {Science},
  volume = {343},
  issue = {6172},
  pages = {764-768},
  year = {2014},
  month = {02},
  doi = {10.1126/science.1244392},
  pmid = {24531968},
  issn = {0036-8075, 1095-9203},
  isbn = {},
  url = {https://doi.org/10.1126/science.1244392},
  publisher = {American Association for the Advancement of Science}
}
@article{louise_guillon_2f979930,
  author = {Louise Guillon AND Joël Chavas AND A. Bénézit AND Marie‐Laure Moutard AND Pauline Roca AND Charles Mellerio AND Catherine Oppenheim AND Denis Rivière AND Jean‐François Mangin},
  title = {Identification of rare cortical folding patterns using unsupervised deep learning},
  abstract = {Abstract Like fingerprints, cortical folding patterns are unique to each brain even though they follow a general species-specific organization. Some folding patterns have been linked with neurodevelopmental disorders. However, due to the high inter-individual variability, the identification of rare folding patterns that could become biomarkers remains a very complex task. This paper proposes a novel unsupervised deep learning approach to identify rare folding patterns and assess the degree of deviations that can be detected. To this end, we preprocess the brain MR images to focus the learning on the folding morphology and train a beta variational auto-encoder (β−VAE) on the inter-individual variability of the folding to identify outliers. We compare the detection power of the latent space and of the reconstruction errors, using synthetic benchmarks and one actual rare configuration related to the central sulcus. Finally, we assess the generalization of our method on a developmental anomaly located in another region and we validate the relevance of our approach on patients suffering from drug-resistant epilepsy. Our results suggest that this method enables encoding relevant folding characteristics that can be enlightened and better interpreted based on the generative power of the β−VAE. The latent space and the reconstruction errors bring complementary information and enable the identification of rare patterns of different nature. This method generalizes well to a different region on another dataset and demonstrates promising results on the epileptic patients. Code is available at https://github.com/neurospin-projects/2022_lguillon_rare_folding_detection.},
  journal = {Imaging Neuroscience},
  volume = {2},
  pages = {1-27},
  year = {2024},
  month = {01},
  doi = {10.1162/imag_a_00084},
  issn = {2837-6056},
  isbn = {},
  url = {https://doi.org/10.1162/imag_a_00084},
  publisher = {The MIT Press}
}
@article{katrin_amunts_a55abf4f,
  author = {Katrin Amunts AND Axel Schleicher AND Karl Zilles},
  title = {Cytoarchitecture of the cerebral cortex—More than localization},
  journal = {NeuroImage},
  volume = {37},
  issue = {4},
  pages = {1061-1065},
  year = {2007},
  month = {03},
  doi = {10.1016/j.neuroimage.2007.02.037},
  pmid = {17870622},
  issn = {1053-8119, 1095-9572},
  isbn = {},
  url = {https://doi.org/10.1016/j.neuroimage.2007.02.037},
  publisher = {Elsevier BV}
}
@article{z__y__sun_ff15abda,
  author = {Z. Y. Sun AND Philippe Pinel AND Denis Rivière AND A. Moreno AND Stanislas Dehaene AND J.-F. Mangin},
  title = {Linking morphological and functional variability in hand movement and silent reading},
  journal = {Brain Structure and Function},
  volume = {221},
  issue = {7},
  pages = {3361-3371},
  year = {2015},
  month = {09},
  doi = {10.1007/s00429-015-1106-8},
  pmid = {26346119},
  issn = {1863-2653, 1863-2661},
  isbn = {},
  url = {https://doi.org/10.1007/s00429-015-1106-8},
  publisher = {Springer Science+Business Media}
}
@article{charles_mellerio_2cce046d,
  author = {Charles Mellerio AND Pauline Roca AND Francine Chassoux AND Florian Danière AND Arnaud Cachia AND Stéphanie Lion AND Olivier Naggara AND Bertrand Devaux AND Jean‐François Meder AND Catherine Oppenheim},
  title = {The Power Button Sign: A Newly Described Central Sulcal Pattern on Surface Rendering MR Images of Type 2 Focal Cortical Dysplasia},
  abstract = {To compare the occurrence of several central sulcus variants and to assess the reproducibility of a sulcal pattern named the power button sign (PBS) in patients with type 2 focal cortical dysplasia (FCD2) and healthy control subjects.The local institutional review board approved the study, and written informed consent was waived for patients and was obtained from control subjects. Four readers reviewed three-dimensional (3D) T1-weighted magnetic resonance (MR) images in 37 patients (13 with negative MR imaging findings) with histologically proven FCD2 of the central region and 44 control subjects on the basis of a visual analysis of a 3D reconstruction of cortical folds. They searched for central sulcus variations (interruptions, side branches, and connections) and for a particular sulcal pattern, namely, the interposition of a precentral sulcal segment between the central sulcus and one of its hook-shaped anterior ascending branches (ie, PBS). Inter- and intraobserver reliability, specificity, and sensitivity were calculated.The central sulcus showed a greater number of side branches (P < .001) and was more frequently connected to the precentral sulcus (P < .001) in patients with FCD2 than in control subjects. The PBS was found in 23 (62%) of 37 total patients with FCD2, in six (46%) of 13 with negative MR imaging findings, and in only one control subject. Inter- and intraobserver rates were excellent (0.88 and 0.93, respectively) for the detection of PBS. FCD2 was located either in the depth of the ascending branch of the central sulcus (14 of 23, 61%) or in its immediate vicinity (nine of 23).Given its excellent reproducibility and specificity, the PBS, when present, could become a useful qualitative diagnostic MR criterion of FCD2 in the central region.},
  journal = {Radiology},
  volume = {274},
  issue = {2},
  pages = {500-507},
  year = {2014},
  month = {09},
  doi = {10.1148/radiol.14140773},
  pmid = {25243540},
  issn = {0033-8419, 1527-1315},
  isbn = {},
  url = {https://doi.org/10.1148/radiol.14140773},
  publisher = {Radiological Society of North America}
}
@article{ralica_dimitrova_8112a008,
  author = {Ralica Dimitrova AND Maximilian Pietsch AND Judit Ciarrusta AND Sean P. Fitzgibbon AND Logan Z. J. Williams AND Daan Christiaens AND Lucilio Cordero‐Grande AND Dafnis Batallé AND Antonios Makropoulos AND Andreas Schuh AND Anthony N. Price AND Jana Hutter AND Rui Pedro A. G. Teixeira AND Emer Hughes AND Andrew Chew AND Shona Falconer AND Olivia Carney AND Alexia Egloff AND Jacques‐Donald Tournier AND Gráinne McAlonan AND Mary Rutherford AND Serena J. Counsell AND Emma C. Robinson AND Joseph V. Hajnal AND Daniel Rueckert AND A. David Edwards AND Jonathan O’Muircheartaigh},
  title = {Preterm birth alters the development of cortical microstructure and morphology at term-equivalent age},
  abstract = {The dynamic nature and complexity of the cellular events that take place during the last trimester of pregnancy make the developing cortex particularly vulnerable to perturbations. Abrupt interruption to normal gestation can lead to significant deviations to many of these processes, resulting in atypical trajectory of cortical maturation in preterm birth survivors. We sought to first map typical cortical micro- and macrostructure development using invivo MRI in a large sample of healthy term-born infants scanned after birth (n = 259). Then we offer a comprehensive characterization of the cortical consequences of preterm birth in 76 preterm infants scanned at term-equivalent age (37–44 weeks postmenstrual age). We describe the group-average atypicality, the heterogeneity across individual preterm infants, and relate individual deviations from normative development to age at birth and neurodevelopment at 18 months. In the term-born neonatal brain, we observed heterogeneous and regionally specific associations between age at scan and measures of cortical morphology and microstructure, including rapid surface expansion, greater cortical thickness, lower cortical anisotropy and higher neurite orientation dispersion. By term-equivalent age, preterm infants had on average increased cortical tissue water content and reduced neurite density index in the posterior parts of the cortex, and greater cortical thickness anteriorly compared to term-born infants. While individual preterm infants were more likely to show extreme deviations (over 3.1 standard deviations) from normative cortical maturation compared to term-born infants, these extreme deviations were highly variable and showed very little spatial overlap between individuals. Measures of regional cortical development were associated with age at birth, but not with neurodevelopment at 18 months. We showed that preterm birth alters cortical micro- and macrostructural maturation near the time of full-term birth. Deviations from normative development were highly variable between individual preterm infants.},
  journal = {NeuroImage},
  volume = {243},
  pages = {118488-118488},
  year = {2021},
  month = {08},
  doi = {10.1016/j.neuroimage.2021.118488},
  pmid = {34419595},
  issn = {1053-8119, 1095-9572},
  isbn = {},
  url = {https://doi.org/10.1016/j.neuroimage.2021.118488},
  publisher = {Elsevier BV}
}
@article{arnaud_cachia_fd5d88e5,
  author = {Arnaud Cachia AND Grégoire Borst AND Cloélia Tissier AND Caroline A. Fisher AND Marion Plaze AND Olivier Gay AND Denis Rivière AND Nitin Gogtay AND Jay N. Giedd AND J.-F. Mangin AND Olivier Houdé AND Armin Raznahan},
  title = {Longitudinal stability of the folding pattern of the anterior cingulate cortex during development},
  abstract = {Prenatal processes are likely critical for the differences in cognitive ability and disease risk that unfold in postnatal life. Prenatally established cortical folding patterns are increasingly studied as an adult proxy for earlier development events – under the as yet untested assumption that an individual's folding pattern is developmentally fixed. Here, we provide the first empirical test of this stability assumption using 263 longitudinally-acquired structural MRI brain scans from 75 typically developing individuals spanning ages 7 to 32 years. We focus on the anterior cingulate cortex (ACC) – an intensely studied cortical region that presents two qualitatively distinct and reliably classifiable sulcal patterns with links to postnatal behavior. We show – without exception–that individual ACC sulcal patterns are fixed from childhood to adulthood, at the same time that quantitative anatomical ACC metrics are undergoing profound developmental change. Our findings buttress use of folding typology as a postnatally-stable marker for linking variations in early brain development to later neurocognitive outcomes in ex utero life.},
  journal = {Developmental Cognitive Neuroscience},
  volume = {19},
  pages = {122-127},
  year = {2016},
  month = {03},
  doi = {10.1016/j.dcn.2016.02.011},
  pmid = {26974743},
  issn = {1878-9293, 1878-9307},
  isbn = {},
  url = {https://doi.org/10.1016/j.dcn.2016.02.011},
  publisher = {Elsevier BV}
}
@misc{zolt_n_moln_r_eeb30ff8,
  author = {Zoltàn Molnàr AND Gavin J. Clowry AND Nenad Šestan AND Ayman Alzu’bi AND Trygve E. Bakken AND Robert F. Hevner AND Petra S. Hüppi AND Ivica Kostović AND Pasko Rakić AND E.S. Anton AND A. David Edwards AND Patrícia P. Garcez AND Anna Hoerder‐Suabedissen AND Arnold R. Kriegstein},
  title = {New insights into the development of the human cerebral cortex},
  abstract = {The cerebral cortex constitutes more than half the volume of the human brain and is presumed to be responsible for the neuronal computations underlying complex phenomena, such as perception, thought, language, attention, episodic memory and voluntary movement. Rodent models are extremely valuable for the investigation of brain development, but cannot provide insight into aspects that are unique or highly derived in humans. Many human psychiatric and neurological conditions have developmental origins but cannot be studied adequately in animal models. The human cerebral cortex has some unique genetic, molecular, cellular and anatomical features, which need to be further explored. The Anatomical Society devoted its summer meeting to the topic of Human Brain Development in June 2018 to tackle these important issues. The meeting was organized by Gavin Clowry (Newcastle University) and Zoltán Molnár (University of Oxford), and held at St John's College, Oxford. The participants provided a broad overview of the structure of the human brain in the context of scaling relationships across the brains of mammals, conserved principles and recent changes in the human lineage. Speakers considered how neuronal progenitors diversified in human to generate an increasing variety of cortical neurons. The formation of the earliest cortical circuits of the earliest generated neurons in the subplate was discussed together with their involvement in neurodevelopmental pathologies. Gene expression networks and susceptibility genes associated to neurodevelopmental diseases were discussed and compared with the networks that can be identified in organoids developed from induced pluripotent stem cells that recapitulate some aspects of in vivo development. New views were discussed on the specification of glutamatergic pyramidal and γ-aminobutyric acid (GABA)ergic interneurons. With the advancement of various in vivo imaging methods, the histopathological observations can be now linked to in vivo normal conditions and to various diseases. Our review gives a general evaluation of the exciting new developments in these areas. The human cortex has a much enlarged association cortex with greater interconnectivity of cortical areas with each other and with an expanded thalamus. The human cortex has relative enlargement of the upper layers, enhanced diversity and function of inhibitory interneurons and a highly expanded transient subplate layer during development. Here we highlight recent studies that address how these differences emerge during development focusing on diverse facets of our evolution.},
  journal = {Journal of Anatomy},
  volume = {235},
  issue = {3},
  pages = {432-451},
  year = {2019},
  month = {08},
  doi = {10.1111/joa.13055},
  pmid = {31373394},
  issn = {0021-8782, 1469-7580},
  isbn = {},
  url = {https://doi.org/10.1111/joa.13055},
  publisher = {Wiley}
}
@article{hiroaki_hashimoto_d6587a9f,
  author = {Hiroaki Hashimoto AND Osamu Takemoto AND Keisuke Nishimoto AND Gento Moriguchi AND Motoki Nakamura AND Yasuyoshi Chiba},
  title = {Normal growth curve of choroid plexus in children: implications for assessing hydrocephalus due to choroid plexus hyperplasia},
  abstract = {OBJECTIVE Pediatric hydrocephalus requires evaluation while accounting for growth of the intracranial structures, but information on choroid plexus growth in children is lacking. This study aimed to create normal growth curves for intracranial volume, choroid plexus volume, and lateral ventricles volume. Additionally, the authors aimed to objectively assess the degree of hydrocephalus caused by choroid plexus hyperplasia (CPH) and to examine the impact of surgical procedures. METHODS This retrospective study analyzed the head CT scans of pediatric patients with minor head trauma treated at Osaka Women’s and Children’s Hospital between March 2006 and May 2023. The study segmented and calculated intracranial, choroid plexus, and lateral ventricles volumes. The study also calculated the correlation coefficients among these 3 parameters. Patients aged 0 to 10 years were divided into 15 age-related clusters, and mean ± SD values were calculated for each cluster. Growth curves were created by plotting mean values sequentially. Volume obtained from patients with CPH were z-normalized using mean and SD values and compared. RESULTS A total of 229 CT scans (94 from females) were analyzed, and positive correlations were observed among intracranial volume, choroid plexus volume, and lateral ventricles volume, with the strongest correlation between the choroid plexus and lateral ventricles volumes. The growth rate of intracranial volume was rapid until approximately 20 months of age, while those of choroid plexus volume and lateral ventricles volume increased rapidly until approximately 1 year of age. Subsequently, choroid plexus volume and lateral ventricles volume plateaued at 1.5 ml and 10 ml, respectively. Three patients with CPH were enrolled and quantitatively evaluated on the basis of the z-normalized volume. Notable abnormal volumes of the choroid plexus (range z-normalized values 24.11–51.17) and lateral ventricles (46.78–122.36) were observed. In 2 patients, improvements in the z-normalized values of intracranial volume and lateral ventricles volume were observed after surgical interventions. Additionally, in 1 patient, choroid plexus volume was reduced by approximately 24% (range z-normalized values 51.17–38.93) after bilateral endoscopic plexus coagulation. CONCLUSIONS This study provides normal growth curves for intracranial volume, choroid plexus volume, and lateral ventricles volume. Knowledge of these normal values holds the potential for objective assessment of abnormal values associated with hydrocephalus and choroid plexus diseases such as CPH.},
  journal = {Journal of Neurosurgery Pediatrics},
  pages = {1-11},
  year = {2023},
  month = {09},
  doi = {10.3171/2023.7.peds23218},
  pmid = {37724840},
  issn = {1933-0707, 1933-0715},
  isbn = {},
  url = {https://doi.org/10.3171/2023.7.peds23218}
}
@inproceedings{seyed_sadegh_mohseni_salehi_3c4df129,
  author = {Seyed Sadegh Mohseni Salehi AND Seyed Raein Hashemi AND Clemente Velasco‐Annis AND Abdelhakim Ouaalam AND Judy A. Estroff AND Deniz Erdoğmuş AND Simon K. Warfield AND Ali Gholipour},
  title = {Real-time automatic fetal brain extraction in fetal MRI by deep learning},
  abstract = {Brain segmentation is a fundamental first step in neuroimage analysis. In the case of fetal MRI, it is particularly challenging and important due to the arbitrary orientation of the fetus, organs that surround the fetal head, and intermittent fetal motion. Several promising methods have been proposed but are limited in their performance in challenging cases and in real-time segmentation. We aimed to develop a fully automatic segmentation method that independently segments sections of the fetal brain in 2D fetal MRI slices in real-time. To this end, we developed and evaluated a deep fully convolutional neural network based on 2D U-net and autocontext, and compared it to two alternative fast methods based on 1) a voxelwise fully convolutional network and 2) a method based on SIFT features, random forest and conditional random field. We trained the networks with manual brain masks on 250 stacks of training images, and tested on 17 stacks of normal fetal brain images as well as 18 stacks of extremely challenging cases based on extreme motion, noise, and severely abnormal brain shape. Experimental results show that our U-net approach outperformed the other methods and achieved average Dice metrics of 96.52% and 78.83% in the normal and challenging test sets, respectively. With an unprecedented performance and a test run time of about 1 second, our network can be used to segment the fetal brain in real-time while fetal MRI slices are being acquired. This can enable real-time motion tracking, motion detection, and 3D reconstruction of fetal brain MRI.},
  journal = {2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)},
  pages = {720-724},
  year = {2018},
  month = {04},
  doi = {10.1109/isbi.2018.8363675},
  issn = {},
  isbn = {},
  url = {https://doi.org/10.1109/isbi.2018.8363675}
}
@article{andrea_urru_8f362c12,
  author = {Andrea Urru AND Oualid Benkarim AND Gerard Martí‐Juan AND N.M. Hahner AND Gemma Piella AND E. Eixarch AND Miguel A. González Ballester},
  title = {Longitudinal Assessment of Abnormal Cortical Folding in Fetuses and Neonates With Isolated Non‐Severe Ventriculomegaly},
  abstract = {ABSTRACT Purpose The impact of ventriculomegaly (VM) on cortical development and brain functionality has been extensively explored in existing literature. VM has been associated with higher risks of attention‐deficit and hyperactivity disorders, as well as cognitive, language, and behavior deficits. Some studies have also shown a relationship between VM and cortical overgrowth, along with reduced cortical folding, both in fetuses and neonates. However, there is a lack of longitudinal studies that study this relationship from fetuses to neonates. Method We used a longitudinal dataset of 30 subjects (15 healthy controls and 15 subjects diagnosed with isolated non‐severe VM (INSVM)) with structural MRI acquired in and ex utero for each subject. We focused on the impact of fetal INSVM on cortical development from a longitudinal perspective, from the fetal to the neonatal stage. Particularly, we examined the relationship between ventricular enlargement and both volumetric features and a multifaceted set of cortical folding measures, including local gyrification, sulcal depth, curvature, and cortical thickness. Findings Our results show significant effects of isolated non‐severe VM (INSVM) compared to healthy controls, with reduced cortical thickness in specific brain regions such as the occipital, parietal, and frontal lobes. Conclusion These findings align with existing literature, confirming the presence of alterations in cortical growth and folding in subjects with isolated non‐severe VM (INSVM) from the fetal to neonatal stage compared to controls.},
  journal = {Brain and Behavior},
  volume = {15},
  issue = {1},
  year = {2025},
  month = {01},
  doi = {10.1002/brb3.70255},
  pmid = {39832168},
  issn = {2162-3279},
  isbn = {},
  url = {https://doi.org/10.1002/brb3.70255},
  publisher = {Wiley}
}
@article{julia_a__scott_4a49589d,
  author = {Julia A. Scott AND Piotr A. Habas AND Vidya Rajagopalan AND Kio Kim AND A. James Barkovich AND Orit A. Glenn AND Colin Studholme},
  title = {Volumetric and surface-based 3D MRI analyses of fetal isolated mild ventriculomegaly},
  journal = {Brain Structure and Function},
  volume = {218},
  issue = {3},
  pages = {645-655},
  year = {2012},
  month = {04},
  doi = {10.1007/s00429-012-0418-1},
  pmid = {22547094},
  issn = {1863-2653, 1863-2661},
  isbn = {},
  url = {https://doi.org/10.1007/s00429-012-0418-1},
  publisher = {Springer Science+Business Media}
}
@article{daniel_cromb_707e6254,
  author = {Daniel Cromb AND Siân Wilson AND Alexandra F Bonthrone AND Andrew Chew AND Christopher Kelly AND Manu Kumar AND Paul Cawley AND Ralica Dimitrova AND Tomoki Arichi AND J Donald Tournier AND Kuberan Pushparajah AND John Simpson AND Mary Rutherford AND Joseph V Hajnal AND A David Edwards AND Chiara Nosarti AND Jonathan O’Muircheartaigh AND Serena J Counsell},
  title = {Individualized cortical gyrification in neonates with congenital heart disease},
  abstract = {Abstract Congenital heart disease is associated with impaired early brain development and adverse neurodevelopmental outcomes. This study investigated how individualized measures of preoperative cortical gyrification index differ in 142 infants with congenital heart disease, using a normative modelling approach with reference data from 320 typically developing infants. Gyrification index Z-scores for the whole brain and six major cortical areas were generated using two different normative models: one accounting for post-menstrual age at scan, post-natal age at scan and sex, and another additionally accounting for supratentorial brain volume. These Z-scores were compared between congenital heart disease and control groups to test the hypothesis that cortical folding in infants with congenital heart disease deviates from the normal developmental trajectory. The relationships between whole-brain gyrification index Z-scores from the two normative models and both cerebral oxygen delivery and neurodevelopmental outcomes were also investigated. Global and regional brain gyrification was significantly reduced in neonates with congenital heart disease, but not when supratentorial brain volume was accounted for. This finding suggests that whilst cortical folding is reduced in congenital heart disease, it is primarily driven by a reduction in brain size. There was a significant positive correlation between cerebral oxygen delivery and whole-brain gyrification index Z-scores in congenital heart disease, but not when supratentorial brain volume was accounted for. Cerebral oxygen delivery is therefore likely to play a more important role in the biological processes underlying volumetric brain growth than cortical folding. No significant associations between whole-brain gyrification index Z-scores and motor/cognitive outcomes or autism traits were identified in the 70 infants with congenital heart disease who underwent neurodevelopmental assessment at 22-months. Our results suggest that chronic in utero and early post-natal hypoxia in congenital heart disease is associated with reductions in cortical folding that are proportional to reductions in supratentorial brain volume.},
  journal = {Brain Communications},
  volume = {6},
  number = {5},
  year = {2024},
  month = {10},
  doi = {10.1093/braincomms/fcae356},
  pmid = {39429246},
  issn = {2632-1297},
  isbn = {},
  url = {https://doi.org/10.1093/braincomms/fcae356},
  publisher = {Oxford University Press},
  urldate = {2024-11-25},
  file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/YV4XBBZT/Cromb et al. - 2024 - Individualized cortical gyrification in neonates with congenital heart disease.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/6NMWH95H/7814779.html:text/html}
}
@article{nathalie_h_p__claessens_815dafd7,
  author = {Nathalie H.P. Claessens AND Pim Moeskops AND Andreas Buchmann AND Beatrice Latal AND Walter Knirsch AND Ianina Scheer AND Ivana Išgum AND Linda S. de Vries AND Manon J.N.L. Benders AND Michael von Rhein},
  title = {Delayed cortical gray matter development in neonates with severe congenital heart disease},
  journal = {Pediatric Research},
  volume = {80},
  issue = {5},
  pages = {668-674},
  year = {2016},
  month = {07},
  doi = {10.1038/pr.2016.145},
  pmid = {27434120},
  issn = {0031-3998, 1530-0447},
  isbn = {},
  url = {https://doi.org/10.1038/pr.2016.145},
  publisher = {Springer Nature}
}
@article{c_dric_clouchoux_b4750266,
  author = {Cédric Clouchoux AND Adré J. du Plessis AND Marine Bouyssi-Kobar AND Wayne Tworetzky AND Doff B. McElhinney AND David W. Brown AND Ali Gholipour AND Dimitri Kudelski AND Simon K. Warfield AND Robert McCarter AND Richard L. Robertson AND Alan C. Evans AND Jane W. Newburger AND Catherine Limperopoulos},
  title = {Delayed Cortical Development in Fetuses with Complex Congenital Heart Disease},
  abstract = {Neurologic impairment is a major complication of complex congenital heart disease (CHD). A growing body of evidence suggests that neurologic dysfunction may be present in a significant proportion of this high-risk population in the early newborn period prior to surgical interventions. We recently provided the first evidence that brain growth impairment in fetuses with complex CHD has its origins in utero. Here, we extend these observations by characterizing global and regional brain development in fetuses with hypoplastic left heart syndrome (HLHS), one of the most severe forms of CHD. Using advanced magnetic resonance imaging techniques, we compared in vivo brain growth in 18 fetuses with HLHS and 30 control fetuses from 25.4–37.0 weeks of gestation. Our findings demonstrate a progressive third trimester fall-off in cortical gray and white matter volumes (P < 0.001), and subcortical gray matter (P < 0.05) in fetuses with HLHS. Significant delays in cortical gyrification were also evident in HLHS fetuses (P < 0.001). In the HLHS fetus, local cortical folding delays were detected as early as 25 weeks in the frontal, parietal, calcarine, temporal, and collateral regions and appear to precede volumetric brain growth disturbances, which may be an early marker of elevated risk for third trimester brain growth failure.},
  journal = {Cerebral Cortex},
  volume = {23},
  issue = {12},
  pages = {2932-2943},
  year = {2012},
  month = {09},
  doi = {10.1093/cercor/bhs281},
  pmid = {22977063},
  issn = {1047-3211, 1460-2199},
  isbn = {},
  url = {https://doi.org/10.1093/cercor/bhs281},
  publisher = {Oxford University Press}
}
@article{tracy_a__glauser_39186bdc,
  author = {Tracy A. Glauser AND Lucy B. Rorke AND Paul M. Weinberg AND Robert R. Clancy},
  title = {Congenital Brain Anomalies Associated With the Hypoplastic Left Heart Syndrome},
  abstract = {This study details the type, frequency, and clinical presentation of developmental brain anomalies in 41 infants with the hypoplastic left heart syndrome encountered during a 52-month interval. Overall, 29% of the infants had either a major or minor central nervous system abnormality. Overt central nervous system malformations, including 3 cases of agenesis of the corpus callosum and 1 case of holoprosencephaly, were seen in 4 infants (10%). Micrencephaly (brain weight at autopsy more than 2 SDs below the mean for age) was found in 27% of the infants. An immature cortical mantle was seen in 21% of the study group. Seven infants (17%) had specific recognizable patterns of malformation. The absence of dysmorphic physical features did not preclude overt or subtle central nervous system malformations. Conversely, the presence of dysmorphic features did not reliably indicate an underlying brain anomaly. Infants who had hypoplastic left heart syndrome as one of multiple nonneurologic malformations were more likely to have micrencephaly than those infants with hypoplastic left heart syndrome as an isolated abnormality. Occurrence of developmental neuropathology was elevated in those infants with hypoplastic left heart syndrome who did not have a recognizable pattern of malformation but who were small for gestational age, microcephalic, or had ocular abnormalities. Infants with hypoplastic left heart syndrome deserve careful genetic, ophthalmologic, and neurologic evaluations, imaging of their intracranial anatomy, and long-term neurologic follow-up.},
  journal = {PEDIATRICS},
  volume = {85},
  issue = {6},
  pages = {984-990},
  year = {1990},
  month = {06},
  doi = {10.1542/peds.85.6.984},
  pmid = {2339047},
  issn = {0031-4005, 1098-4275},
  isbn = {},
  url = {https://doi.org/10.1542/peds.85.6.984},
  publisher = {American Academy of Pediatrics}
}
@article{carolin_gaiser_22ec8ed8,
  author = {Carolin Gaiser AND Pierre Berthet AND Seyed Mostafa Kia AND Maarten A. Frens AND Christian F. Beckmann AND Ryan L. Muetzel AND André F. Marquand},
  title = {Estimating cortical thickness trajectories in children across different scanners using transfer learning from normative models},
  abstract = {This work illustrates the use of normative models in a longitudinal neuroimaging study of children aged 6-17 years and demonstrates how such models can be used to make meaningful comparisons in longitudinal studies, even when individuals are scanned with different scanners across successive study waves. More specifically, we first estimated a large-scale reference normative model using Hierarchical Bayesian Regression from N = 42,993 individuals across the lifespan and from dozens of sites. We then transfer these models to a longitudinal developmental cohort (N = 6285) with three measurement waves acquired on two different scanners that were unseen during estimation of the reference models. We show that the use of normative models provides individual deviation scores that are independent of scanner effects and efficiently accommodate inter-site variations. Moreover, we provide empirical evidence to guide the optimization of sample size for the transfer of prior knowledge about the distribution of regional cortical thicknesses. We show that a transfer set containing as few as 25 samples per site can lead to good performance metrics on the test set. Finally, we demonstrate the clinical utility of this approach by showing that deviation scores obtained from the transferred normative models are able to detect and chart morphological heterogeneity in individuals born preterm.},
  journal = {Human Brain Mapping},
  volume = {45},
  issue = {2},
  year = {2024},
  month = {02},
  doi = {10.1002/hbm.26565},
  pmid = {38339954},
  issn = {1065-9471, 1097-0193},
  isbn = {},
  url = {https://doi.org/10.1002/hbm.26565},
  publisher = {Wiley}
}
@article{catherine_limperopoulos_2ef5c724,
  author = {Catherine Limperopoulos AND Gevorg Chilingaryan AND Nancy Sullivan AND Nicolas Guizard AND Richard L. Robertson AND Adré J. du Plessis},
  title = {Injury to the Premature Cerebellum: Outcome is Related to Remote Cortical Development},
  abstract = {Cerebellar injury is an important complication of preterm birth with far-reaching neuropsychiatric sequelae. We have previously shown a significant association between isolated injury to the premature cerebellum and subsequent impairment of regional volumetric growth in the contralateral cerebrum. In the current study, we examine the relationship between these remote regional impairments of cerebral volumetric growth and domain-specific functional deficits in these children. In 40 ex-preterm infants with isolated cerebellar injury, we performed neurodevelopmental evaluations and quantitative magnetic resonance imaging (MRI) studies at a mean age of 34 months. We measured cortical gray matter volumes in 8 parcellated regions of each cerebral hemisphere, as well as right and left cerebellar volumes. We show highly significant associations between early signs of autism and dorsolateral prefrontal cortex volume (P < 0.001); gross motor scores and sensorimotor cortical volumes (P < 0.001); and cognitive and expressive language scores and premotor and mid-temporal cortical volumes (P < 0.001). By multivariate analyses, each unit increase in the corresponding regional cerebral volume was associated with lower odds of abnormal outcome score, adjusted for age at MRI and contralateral cerebellar volume. This is the first report linking secondary impairment of remote cerebral cortical growth and functional disabilities in survivors of prematurity-related cerebellar brain injury.},
  journal = {Cerebral Cortex},
  volume = {24},
  issue = {3},
  pages = {728-736},
  year = {2012},
  month = {11},
  doi = {10.1093/cercor/bhs354},
  pmid = {23146968},
  issn = {1047-3211, 1460-2199},
  isbn = {},
  url = {https://doi.org/10.1093/cercor/bhs354},
  publisher = {Oxford University Press}
}
@article{erin_engelhardt_c20a28a5,
  author = {Erin Engelhardt AND Terrie E. Inder AND Dimitrios Alexopoulos AND Donna Dierker AND Jason E. Hill AND David C. Van Essen AND Jeffrey J. Neil},
  title = {Regional impairments of cortical folding in premature infants},
  abstract = {This study was undertaken to evaluate the influence of preterm birth and other factors on cerebral cortical maturation.We have evaluated the effects of preterm birth on cortical folding by applying cortical cartography methods to a cohort of 52 preterm infants (<31 weeks gestation, mild or no injury on conventional magnetic resonance imaging) and 12 term-born control infants. All infants were evaluated at term-equivalent postmenstrual age.Preterm infants had lower values for the global measures of gyrification index (GI; 2.06 ± 0.07 vs 1.80 ± 0.12, p < 0.001; control vs preterm) and cortical surface area (CSA; 316 ± 24 cm(2) vs 257 ± 40 cm(2) , p < 0.001). Regional analysis of sulcal depth and cortical shape showed the greatest impact of preterm birth on the insula, superior temporal sulcus, and ventral portions of the pre- and postcentral sulci in both hemispheres. Although CSA and GI are related, CSA was more sensitive to antenatal and postnatal factors than GI. Both measures were lower in preterm infants of lower birth weight standard deviation scores and smaller occipitofrontal circumference at time of scan, whereas CSA alone was lower in association with smaller occipitofrontal circumference at birth. CSA was also lower in infants with higher critical illness in the first 24 hours of life, exposure to postnatal steroids, and prolonged endotracheal intubation.Preterm birth disrupts cortical development in a regionally specific fashion with abnormalities evident by term-equivalent postmenstrual age. This disruption is influenced by both antenatal growth and postnatal course.},
  journal = {Annals of Neurology},
  volume = {77},
  issue = {1},
  pages = {154-162},
  year = {2014},
  month = {11},
  doi = {10.1002/ana.24313},
  pmid = {25425403},
  issn = {0364-5134, 1531-8249},
  isbn = {},
  url = {https://doi.org/10.1002/ana.24313},
  publisher = {Wiley}
}
@online{yourong_guo_d0b4579d,
  author = {Yourong Guo AND Logan Williams AND Russell Macleod AND Emma Robinson AND Jonathan O'Muircheartaigh},
  title = {Normative modelling of cortical folding in term and preterm born neonates},
  year = {2023},
  month = {01},
  issn = {},
  isbn = {}
}
@article{sophie_adler_5f11ec03,
  author = {Sophie Adler AND Konrad Wagstyl AND Roxana Gunny AND Lisa Ronan AND David W. Carmichael AND J. Helen Cross AND Paul C. Fletcher AND Torsten Baldeweg},
  title = {Novel surface features for automated detection of focal cortical dysplasias in paediatric epilepsy},
  abstract = {Focal cortical dysplasia is a congenital abnormality of cortical development and the leading cause of surgically remediable drug-resistant epilepsy in children. Post-surgical outcome is improved by presurgical lesion detection on structural MRI. Automated computational techniques have improved detection of focal cortical dysplasias in adults but have not yet been effective when applied to developing brains. There is therefore a need to develop reliable and sensitive methods to address the particular challenges of a paediatric cohort. We developed a classifier using surface-based features to identify focal abnormalities of cortical development in a paediatric cohort. In addition to established measures, such as cortical thickness, grey-white matter blurring, FLAIR signal intensity, sulcal depth and curvature, our novel features included complementary metrics of surface morphology such as local cortical deformation as well as post-processing methods such as the "doughnut" method - which quantifies local variability in cortical morphometry/MRI signal intensity, and per-vertex interhemispheric asymmetry. A neural network classifier was trained using data from 22 patients with focal epilepsy (mean age = 12.1 ± 3.9, 9 females), after intra- and inter-subject normalisation using a population of 28 healthy controls (mean age = 14.6 ± 3.1, 11 females). Leave-one-out cross-validation was used to quantify classifier sensitivity using established features and the combination of established and novel features. Focal cortical dysplasias in our paediatric cohort were correctly identified with a higher sensitivity (73%) when novel features, based on our approach for detecting local cortical changes, were included, when compared to the sensitivity using only established features (59%). These methods may be applicable to aiding identification of subtle lesions in medication-resistant paediatric epilepsy as well as to the structural analysis of both healthy and abnormal cortical development.},
  journal = {NeuroImage Clinical},
  volume = {14},
  pages = {18-27},
  year = {2016},
  month = {12},
  doi = {10.1016/j.nicl.2016.12.030},
  pmid = {28123950},
  issn = {2213-1582},
  isbn = {},
  url = {https://doi.org/10.1016/j.nicl.2016.12.030},
  publisher = {Elsevier BV}
}
@article{arnaud_cachia_6725b027,
  author = {Arnaud Cachia AND Grégoire Borst AND Renaud Jardri AND Armin Raznahan AND Graham K. Murray AND Jean‐François Mangin AND Marion Plaze},
  title = {Towards Deciphering the Fetal Foundation of Normal Cognition and Cognitive Symptoms From Sulcation of the Cortex},
  abstract = {Growing evidence supports that prenatal processes play an important role for cognitive ability in normal and clinical conditions. In this context, several neuroimaging studies searched for features in postnatal life that could serve as a proxy for earlier developmental events. A very interesting candidate is the sulcal, or sulco-gyral, patterns, macroscopic features of the cortex anatomy related to the fold topology—e.g., continuous vs. interrupted/broken fold, present vs. absent fold-or their spatial organization. Indeed, as opposed to quantitative features of the cortical sheet (e.g., thickness, surface area or curvature) taking decades to reach the levels measured in adult, the qualitative sulcal patterns are mainly determined before birth and stable across the lifespan. The sulcal patterns therefore offer a window on the fetal constraints on specific brain areas on cognitive abilities and clinical symptoms that manifest later in life. After a global review of the cerebral cortex sulcation, its mechanisms, its ontogenesis along with methodological issues on how to measure the sulcal patterns, we present a selection of studies illustrating that analysis of the sulcal patterns can provide information on prenatal dispositions to cognition (with a focus on cognitive control and academic abilities) and cognitive symptoms (with a focus on schizophrenia and bipolar disorders). Finally, perspectives of sulcal studies are discussed.},
  journal = {Frontiers in Neuroanatomy},
  volume = {15},
  year = {2021},
  month = {09},
  doi = {10.3389/fnana.2021.712862},
  pmid = {34650408},
  issn = {1662-5129},
  isbn = {},
  url = {https://doi.org/10.3389/fnana.2021.712862},
  publisher = {Frontiers Media}
}
@article{yourong_guo_f5832c80,
  author = {Yourong Guo AND Mohamed A. Suliman AND Logan Z. J. Williams AND Renátó Besenczi AND Kaili Liang AND Simon Dahan AND Vanessa Kyriakopoulou AND Gráinne McAlonan AND Alexander Hammers AND Jonathan O’Muircheartaigh AND Emma C. Robinson},
  title = {Motifs of brain cortical folding from birth to adulthood: structural asymmetry and folding-functional links},
  abstract = {Cortical folding of brain is widely regarded as an interplay between genetic programming and biomechanical forces, closely linked to cytoarchitectonic regionalisation. Abnormal folding patterns are frequently observed in neurodevelopmental conditions and psychiatric disorders. However, significant inter-individual variability of secondary and tertiary folds obscures detection of shape biomarkers and confounds investigation of folding-functional relationships. Here we investigate cortical folding heterogeneity at fine scale, using novel hierarchical surface registration (MSM-HT) to parse cortical folding patterns into a representative family of distinct anatomical templates. By applying this technique both to young adults from the Human Connectome Project (HCP) and neonates in the Developing HCP and Brain Imaging in Babies (BIBS) cohorts, we identify and characterize common lobe-wise folding patterns: observing consistency across both age groups, with neonatal samples showing less variation. Crucially, we highlight significant hemispheric asymmetry within the temporal lobe for both adults and neonates, and show that improved correspondence of shape does not translate to improved areal correspondence, affirming previous studies that have pointed to dissociation of folding and functional organisation. This study provides a critical step towards understanding brain asymmetry and complex relationships between folding and function, offering a robust framework to generalise the uncovered cortical folding motifs across datasets and developmental stages.},
  journal = {bioRxiv (Cold Spring Harbor Laboratory)},
  year = {2025},
  month = {03},
  doi = {10.1101/2025.03.04.641459},
  issn = {},
  isbn = {},
  url = {https://doi.org/10.1101/2025.03.04.641459},
  publisher = {Cold Spring Harbor Laboratory}
}
@article{cher_bass_4babf27d,
  author = {Cher Bass AND Mariana da Silva AND Carole H. Sudre AND Petru-Daniel Tudosiu AND Stephen M. Smith AND Emma C. Robinson},
  title = {ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping},
  abstract = {Feature attribution (FA), or the assignment of class-relevance to different locations in an image, is important for many classification problems but is particularly crucial within the neuroscience domain, where accurate mechanistic models of behaviours, or disease, require knowledge of all features discriminative of a trait. At the same time, predicting class relevance from brain images is challenging as phenotypes are typically heterogeneous, and changes occur against a background of significant natural variation. Here, we present a novel framework for creating class specific FA maps through image-to-image translation. We propose the use of a VAE-GAN to explicitly disentangle class relevance from background features for improved interpretability properties, which results in meaningful FA maps. We validate our method on 2D and 3D brain image datasets of dementia (ADNI dataset), ageing (UK Biobank), and (simulated) lesion detection. We show that FA maps generated by our method outperform baseline FA methods when validated against ground truth. More significantly, our approach is the first to use latent space sampling to support exploration of phenotype variation. Our code will be available online at https://github.com/CherBass/ICAM.},
  journal = {arXiv (Cornell University)},
  year = {2020},
  month = {01},
  doi = {10.48550/arxiv.2006.08287},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/2006.08287},
  publisher = {Cornell University}
}
@article{florentin_bieder_80901708,
  author = {Florentin Bieder AND Paul Friedrich AND Hélène Corbaz AND Alicia Durrer AND Julia Wolleb AND Philippe C. Cattin},
  title = {Modeling the Neonatal Brain Development Using Implicit Neural
  Representations},
  abstract = {The human brain undergoes rapid development during the third trimester of pregnancy. In this work, we model the neonatal development of the infant brain in this age range. As a basis, we use MR images of preterm- and term-birth neonates from the developing human connectome project (dHCP). We propose a neural network, specifically an implicit neural representation (INR), to predict 2D- and 3D images of varying time points. In order to model a subject-specific development process, it is necessary to disentangle the age from the subjects' identity in the latent space of the INR. We propose two methods, Subject Specific Latent Vectors (SSL) and Stochastic Global Latent Augmentation (SGLA), enabling this disentanglement. We perform an analysis of the results and compare our proposed model to an age-conditioned denoising diffusion model as a baseline. We also show that our method can be applied in a memory-efficient way, which is especially important for 3D data.},
  journal = {arXiv (Cornell University)},
  year = {2024},
  month = {08},
  doi = {10.48550/arxiv.2408.08647},
  issn = {},
  isbn = {},
  url = {http://arxiv.org/abs/2408.08647},
  publisher = {Cornell University}
}


@article{b_t__thomas_yeo_991afc31,
  author = {B.T. Thomas Yeo AND Mert R. Sabuncu AND Tom Vercauteren AND Nicholas Ayache AND Bruce Fischl AND Polina Golland},
  title = {Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration},
  abstract = {We present the Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizors for the modified Demons objective function can be efficiently approximated on the sphere using iterative smoothing. Based on one parameter subgroups of diffeomorphisms, the resulting registration is diffeomorphic and fast. The Spherical Demons algorithm can also be modified to register a given spherical image to a probabilistic atlas. We demonstrate two variants of the algorithm corresponding to warping the atlas or warping the subject. Registration of a cortical surface mesh to an atlas mesh, both with more than 160 k nodes requires less than 5 min when warping the atlas and less than 3 min when warping the subject on a Xeon 3.2 GHz single processor machine. This is comparable to the fastest nondiffeomorphic landmark-free surface registration algorithms. Furthermore, the accuracy of our method compares favorably to the popular FreeSurfer registration algorithm. We validate the technique in two different applications that use registration to transfer segmentation labels onto a new image (1) parcellation of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vivo</i> cortical surfaces and (2) Brodmann area localization in <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ex vivo</i> cortical surfaces.},
  journal = {IEEE Transactions on Medical Imaging},
  volume = {29},
  issue = {3},
  pages = {650-668},
  year = {2009},
  month = {08},
  doi = {10.1109/tmi.2009.2030797},
  pmid = {19709963},
  issn = {0278-0062, 1558-254X},
  isbn = {},
  url = {https://doi.org/10.1109/tmi.2009.2030797},
  publisher = {Institute of Electrical and Electronics Engineers}
}
@misc{bruce_fischl_9de03391,
  author = {Bruce Fischl AND Martin I Sereno AND Anders M Dale},
  title = {Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System},
  year = {1451},
  month = {12},
  issn = {},
  isbn = {},
  url = {https://www.sciencedirect.com/science/article/pii/S1053811998903962}
}
@article{pierre_besson_f59f29bd,
  author = {Pierre Besson AND Todd B. Parrish AND Aggelos K. Katsaggelos AND S. Kathleen Bandt},
  title = {Geometric deep learning on brain shape predicts sex and age},
  journal = {Computerized Medical Imaging and Graphics},
  volume = {91},
  pages = {101939-101939},
  year = {2021},
  month = {05},
  doi = {10.1016/j.compmedimag.2021.101939},
  pmid = {34082280},
  issn = {0895-6111, 1879-0771},
  isbn = {},
  url = {https://doi.org/10.1016/j.compmedimag.2021.101939},
  publisher = {Elsevier BV}
}
@article{simon_dahan_332af9bf,
  author = {Simon Dahan AND Hao Xu AND Logan Z. J. Williams AND Abdulah Fawaz AND Chunhui Yang AND Timothy S. Coalson AND Michelle C. Williams AND David E. Newby AND Anne Edwards AND Matthew F. Glasser AND Alistair A. Young AND Daniel Rueckert AND Emma C. Robinson},
  title = {Surface Vision Transformers: Flexible Attention-Based Modelling of Biomedical Surfaces},
  abstract = {Recent state-of-the-art performances of Vision Transformers (ViT) in computer vision tasks demonstrate that a general-purpose architecture, which implements long-range self-attention, could replace the local feature learning operations of convolutional neural networks. In this paper, we extend ViTs to surfaces by reformulating the task of surface learning as a sequence-to-sequence learning problem, by proposing patching mechanisms for general surface meshes. Sequences of patches are then processed by a transformer encoder and used for classification or regression. We validate our method on a range of different biomedical surface domains and tasks: brain age prediction in the developing Human Connectome Project (dHCP), fluid intelligence prediction in the Human Connectome Project (HCP), and coronary artery calcium score classification using surfaces from the Scottish Computed Tomography of the Heart (SCOT-HEART) dataset, and investigate the impact of pretraining and data augmentation on model performance. Results suggest that Surface Vision Transformers (SiT) demonstrate consistent improvement over geometric deep learning methods for brain age and fluid intelligence prediction and achieve comparable performance on calcium score classification to standard metrics used in clinical practice. Furthermore, analysis of transformer attention maps offers clear and individualised predictions of the features driving each task. Code is available on Github: https://github.com/metrics-lab/surface-vision-transformers},
  journal = {arXiv (Cornell University)},
  year = {2022},
  month = {01},
  doi = {10.48550/arXiv.2204.03408},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/2204.03408},
  publisher = {Cornell University}
}
@article{raffaelli_charles_f410db96,
  author = {Raffaelli Charles AND Hao Su AND Kaichun Mo AND Leonidas Guibas},
  title = {PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation},
  abstract = {Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessarily voluminous and causes issues. In this paper, we design a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of points in the input. Our network, named PointNet, provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing. Though simple, PointNet is highly efficient and effective. Empirically, it shows strong performance on par or even better than state of the art. Theoretically, we provide analysis towards understanding of what the network has learnt and why the network is robust with respect to input perturbation and corruption.},
  pages = {77-85},
  year = {2017},
  month = {07},
  doi = {10.1109/cvpr.2017.16},
  issn = {},
  isbn = {},
  url = {https://doi.org/10.1109/cvpr.2017.16}
}
@article{vitalis_vosylius_437e15ea,
  author = {Vitalis Vosylius AND Andy Wang AND Cemlyn Waters AND Alexey Zakharov AND F. Kingdon Ward AND Loïc Le Folgoc AND John Cupitt AND Antonios Makropoulos AND Andreas Schuh AND Daniel Rueckert AND Amir Alansary},
  title = {Geometric Deep Learning for Post-Menstrual Age Prediction based on the Neonatal White Matter Cortical Surface},
  abstract = {Accurate estimation of the age in neonates is essential for measuring neurodevelopmental, medical, and growth outcomes. In this paper, we propose a novel approach to predict the post-menstrual age (PA) at scan, using techniques from geometric deep learning, based on the neonatal white matter cortical surface. We utilize and compare multiple specialized neural network architectures that predict the age using different geometric representations of the cortical surface; we compare MeshCNN, Pointnet++, GraphCNN, and a volumetric benchmark. The dataset is part of the Developing Human Connectome Project (dHCP), and is a cohort of healthy and premature neonates. We evaluate our approach on 650 subjects (727scans) with PA ranging from 27 to 45 weeks. Our results show accurate prediction of the estimated PA, with mean error less than one week.},
  journal = {arXiv (Cornell University)},
  year = {2020},
  month = {01},
  doi = {10.48550/arXiv.2008.06098},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/2008.06098},
  publisher = {Cornell University}
}
@article{gang_wang_e39a9a18,
  author = {Gang Wang AND Xiaofeng Zhang AND Qingtang Su AND Jie Shi AND Richard J. Caselli AND Yalin Wang},
  title = {A novel cortical thickness estimation method based on volumetric Laplace–Beltrami operator and heat kernel},
  journal = {Medical Image Analysis},
  volume = {22},
  issue = {1},
  pages = {1-20},
  year = {2015},
  month = {02},
  doi = {10.1016/j.media.2015.01.005},
  pmid = {25700360},
  issn = {1361-8415, 1361-8423, 1361-8431},
  isbn = {},
  url = {https://doi.org/10.1016/j.media.2015.01.005},
  publisher = {Elsevier BV}
}
@online{implicit_and_parametric_surfaces_2b12008d,
  author = {},
  title = {Implicit and Parametric Surfaces},
  issn = {},
  isbn = {}
}
@article{william_e__lorensen_16252b0c,
  author = {William E. Lorensen AND H. E. Cline},
  title = {Marching cubes: A high resolution 3D surface construction algorithm},
  abstract = {We present a new algorithm, called marching cubes, that creates triangle models of constant density surfaces from 3D medical data. Using a divide-and-conquer approach to generate inter-slice connectivity, we create a case table that defines triangle topology. The algorithm processes the 3D medical data in scan-line order and calculates triangle vertices using linear interpolation. We find the gradient of the original data, normalize it, and use it as a basis for shading the models. The detail in images produced from the generated surface models is the result of maintaining the inter-slice connectivity, surface data, and gradient information present in the original 3D data. Results from computed tomography (CT), magnetic resonance (MR), and single-photon emission computed tomography (SPECT) illustrate the quality and functionality of marching cubes. We also discuss improvements that decrease processing time and add solid modeling capabilities.},
  pages = {163-169},
  year = {1987},
  month = {08},
  doi = {10.1145/37401.37422},
  issn = {},
  isbn = {},
  url = {https://doi.org/10.1145/37401.37422}
}
@misc{charles_r__qi_73aa1c5a,
  author = {Charles R. Qi AND Hao Su AND Kaichun Mo AND Leonidas J. Guibas},
  title = {PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation},
  abstract = {Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessarily voluminous and causes issues. In this paper, we design a novel type of neural network that directly consumes point clouds and well respects the permutation invariance of points in the input. Our network, named PointNet, provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing. Though simple, PointNet is highly efficient and effective. Empirically, it shows strong performance on par or even better than state of the art. Theoretically, we provide analysis towards understanding of what the network has learnt and why the network is robust with respect to input perturbation and corruption.},
  year = {2017},
  month = {04},
  doi = {10.48550/arXiv.1612.00593},
  issn = {},
  isbn = {},
  url = {http://arxiv.org/abs/1612.00593},
  publisher = {arXiv},
  note = {arXiv:1612.00593 [cs]},
  urldate = {2023-09-06},
  file = {arXiv Fulltext PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/R7HUKCDS/Qi et al. - 2017 - PointNet Deep Learning on Point Sets for 3D Class.pdf:application/pdf;arXiv.org Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/ASZW8X8S/1612.html:text/html},
  keywords = {Computer Science - Computer Vision and Pattern Recognition},
  shorttitle = {PointNet}
}
@article{charles_r__qi_f1935552,
  author = {Charles R. Qi AND Yi Li AND Hao Su AND Leonidas Guibas},
  title = {PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space},
  abstract = {Few prior works study deep learning on point sets. PointNet by Qi et al. is a pioneer in this direction. However, by design PointNet does not capture local structures induced by the metric space points live in, limiting its ability to recognize fine-grained patterns and generalizability to complex scenes. In this work, we introduce a hierarchical neural network that applies PointNet recursively on a nested partitioning of the input point set. By exploiting metric space distances, our network is able to learn local features with increasing contextual scales. With further observation that point sets are usually sampled with varying densities, which results in greatly decreased performance for networks trained on uniform densities, we propose novel set learning layers to adaptively combine features from multiple scales. Experiments show that our network called PointNet++ is able to learn deep point set features efficiently and robustly. In particular, results significantly better than state-of-the-art have been obtained on challenging benchmarks of 3D point clouds.},
  journal = {arXiv (Cornell University)},
  year = {2017},
  month = {01},
  doi = {10.48550/arxiv.1706.02413},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/1706.02413},
  publisher = {Cornell University}
}
@article{zhijian_liu_5000e843,
  author = {Zhijian Liu AND Haotian Tang AND Yujun Lin AND Song Han},
  title = {Point-Voxel CNN for Efficient 3D Deep Learning},
  abstract = {We present Point-Voxel CNN (PVCNN) for efficient, fast 3D deep learning. Previous work processes 3D data using either voxel-based or point-based NN models. However, both approaches are computationally inefficient. The computation cost and memory footprints of the voxel-based models grow cubically with the input resolution, making it memory-prohibitive to scale up the resolution. As for point-based networks, up to 80% of the time is wasted on structuring the sparse data which have rather poor memory locality, not on the actual feature extraction. In this paper, we propose PVCNN that represents the 3D input data in points to reduce the memory consumption, while performing the convolutions in voxels to reduce the irregular, sparse data access and improve the locality. Our PVCNN model is both memory and computation efficient. Evaluated on semantic and part segmentation datasets, it achieves much higher accuracy than the voxel-based baseline with 10x GPU memory reduction; it also outperforms the state-of-the-art point-based models with 7x measured speedup on average. Remarkably, the narrower version of PVCNN achieves 2x speedup over PointNet (an extremely efficient model) on part and scene segmentation benchmarks with much higher accuracy. We validate the general effectiveness of PVCNN on 3D object detection: by replacing the primitives in Frustrum PointNet with PVConv, it outperforms Frustrum PointNet++ by 2.4% mAP on average with 1.5x measured speedup and GPU memory reduction.},
  journal = {arXiv (Cornell University)},
  year = {2019},
  month = {01},
  doi = {10.48550/arxiv.1907.03739},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/1907.03739},
  publisher = {Cornell University}
}
@misc{linqi_zhou_b27f2f2d,
  author = {Linqi Zhou AND Yilun Du AND Jiajun Wu},
  title = {3D Shape Generation and Completion through Point-Voxel Diffusion},
  abstract = {We propose a novel approach for probabilistic generative modeling of 3D shapes. Unlike most existing models that learn to deterministically translate a latent vector to a shape, our model, Point-Voxel Diffusion (PVD), is a unified, probabilistic formulation for unconditional shape generation and conditional, multi-modal shape completion. PVD marries denoising diffusion models with the hybrid, point-voxel representation of 3D shapes. It can be viewed as a series of denoising steps, reversing the diffusion process from observed point cloud data to Gaussian noise, and is trained by optimizing a variational lower bound to the (conditional) likelihood function. Experiments demonstrate that PVD is capable of synthesizing high-fidelity shapes, completing partial point clouds, and generating multiple completion results from single-view depth scans of real objects.},
  year = {2021},
  month = {08},
  doi = {10.48550/arXiv.2104.03670},
  issn = {},
  isbn = {},
  url = {http://arxiv.org/abs/2104.03670},
  publisher = {arXiv},
  urldate = {2023-09-06},
  file = {arXiv Fulltext PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/863AN78Q/Zhou et al. - 2021 - 3D Shape Generation and Completion through Point-V.pdf:application/pdf;arXiv.org Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/FVKJUFYP/2104.html:text/html},
  note = {arXiv:2104.03670 [cs]},
  keywords = {Computer Science - Computer Vision and Pattern Recognition}
}
@misc{xiaohui_zeng_2b1f6003,
  author = {Xiaohui Zeng AND Arash Vahdat AND Francis Williams AND Zan Gojcic AND Or Litany AND Sanja Fidler AND Karsten Kreis},
  title = {LION: Latent Point Diffusion Models for 3D Shape Generation},
  abstract = {Denoising diffusion models (DDMs) have shown promising results in 3D point cloud synthesis. To advance 3D DDMs and make them useful for digital artists, we require (i) high generation quality, (ii) flexibility for manipulation and applications such as conditional synthesis and shape interpolation, and (iii) the ability to output smooth surfaces or meshes. To this end, we introduce the hierarchical Latent Point Diffusion Model (LION) for 3D shape generation. LION is set up as a variational autoencoder (VAE) with a hierarchical latent space that combines a global shape latent representation with a point-structured latent space. For generation, we train two hierarchical DDMs in these latent spaces. The hierarchical VAE approach boosts performance compared to DDMs that operate on point clouds directly, while the point-structured latents are still ideally suited for DDM-based modeling. Experimentally, LION achieves state-of-the-art generation performance on multiple ShapeNet benchmarks. Furthermore, our VAE framework allows us to easily use LION for different relevant tasks: LION excels at multimodal shape denoising and voxel-conditioned synthesis, and it can be adapted for text- and image-driven 3D generation. We also demonstrate shape autoencoding and latent shape interpolation, and we augment LION with modern surface reconstruction techniques to generate smooth 3D meshes. We hope that LION provides a powerful tool for artists working with 3D shapes due to its high-quality generation, flexibility, and surface reconstruction. Project page and code: https://nv-tlabs.github.io/LION.},
  year = {2022},
  month = {10},
  doi = {10.48550/arXiv.2210.06978},
  issn = {},
  isbn = {},
  url = {http://arxiv.org/abs/2210.06978},
  publisher = {arXiv},
  note = {arXiv:2210.06978 [cs, stat]},
  shorttitle = {LION},
  urldate = {2023-09-06},
  file = {arXiv Fulltext PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/ZM8QSUYU/Zeng et al. - 2022 - LION Latent Point Diffusion Models for 3D Shape G.pdf:application/pdf;arXiv.org Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/QB72WQBG/2210.html:text/html},
  keywords = {Computer Science - Machine Learning}
}
@article{zixuan_huang_2bf48cb1,
  author = {Zixuan Huang AND Justin C. Johnson AND Shoubhik Debnath AND James M. Rehg AND Chao-Yuan Wu},
  title = {PointInfinity: Resolution-Invariant Point Diffusion Models},
  abstract = {We present PointInfinity, an efficient family of point cloud diffusion models. Our core idea is to use a transformer-based architecture with a fixed-size, resolution-invariant latent representation. This enables efficient training with low-resolution point clouds, while allowing high-resolution point clouds to be generated during inference. More importantly, we show that scaling the test-time resolution beyond the training resolution improves the fidelity of generated point clouds and surfaces. We analyze this phenomenon and draw a link to classifier-free guidance commonly used in diffusion models, demonstrating that both allow trading off fidelity and variability during inference. Experiments on CO3D show that PointInfinity can efficiently generate high-resolution point clouds (up to 131k points, 31 times more than Point-E) with state-of-the-art quality.},
  journal = {arXiv (Cornell University)},
  year = {2024},
  month = {04},
  doi = {10.48550/arxiv.2404.03566},
  issn = {},
  isbn = {},
  url = {http://arxiv.org/abs/2404.03566},
  publisher = {Cornell University}
}
@article{zechao_guan_0d0928ce,
  author = {Zechao Guan AND Feng Yan AND Shuai Du AND Lin Ma AND Qingshan Liu},
  title = {TopoDiT-3D: Topology-Aware Diffusion Transformer with Bottleneck Structure for 3D Point Cloud Generation},
  abstract = {Recent advancements in Diffusion Transformer (DiT) models have significantly improved 3D point cloud generation. However, existing methods primarily focus on local feature extraction while overlooking global topological information, such as voids, which are crucial for maintaining shape consistency and capturing complex geometries. To address this limitation, we propose TopoDiT-3D, a Topology-Aware Diffusion Transformer with a bottleneck structure for 3D point cloud generation. Specifically, we design the bottleneck structure utilizing Perceiver Resampler, which not only offers a mode to integrate topological information extracted through persistent homology into feature learning, but also adaptively filters out redundant local features to improve training efficiency. Experimental results demonstrate that TopoDiT-3D outperforms state-of-the-art models in visual quality, diversity, and training efficiency. Furthermore, TopoDiT-3D demonstrates the importance of rich topological information for 3D point cloud generation and its synergy with conventional local feature learning. Videos and code are available at https://github.com/Zechao-Guan/TopoDiT-3D.},
  year = {2025},
  month = {01},
  doi = {10.48550/ARXIV.2505.09140},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/2505.09140},
  publisher = {arXiv}
}
@article{shentong_mo_a34d13a8,
  author = {Shentong Mo AND Enze Xie AND Ruihang Chu AND Lewei Yao AND Lanqing Hong AND Matthias Nießner AND Zhenguo Li},
  title = {DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generation},
  abstract = {Recent Diffusion Transformers (e.g., DiT) have demonstrated their powerful effectiveness in generating high-quality 2D images. However, it is still being determined whether the Transformer architecture performs equally well in 3D shape generation, as previous 3D diffusion methods mostly adopted the U-Net architecture. To bridge this gap, we propose a novel Diffusion Transformer for 3D shape generation, namely DiT-3D, which can directly operate the denoising process on voxelized point clouds using plain Transformers. Compared to existing U-Net approaches, our DiT-3D is more scalable in model size and produces much higher quality generations. Specifically, the DiT-3D adopts the design philosophy of DiT but modifies it by incorporating 3D positional and patch embeddings to adaptively aggregate input from voxelized point clouds. To reduce the computational cost of self-attention in 3D shape generation, we incorporate 3D window attention into Transformer blocks, as the increased 3D token length resulting from the additional dimension of voxels can lead to high computation. Finally, linear and devoxelization layers are used to predict the denoised point clouds. In addition, our transformer architecture supports efficient fine-tuning from 2D to 3D, where the pre-trained DiT-2D checkpoint on ImageNet can significantly improve DiT-3D on ShapeNet. Experimental results on the ShapeNet dataset demonstrate that the proposed DiT-3D achieves state-of-the-art performance in high-fidelity and diverse 3D point cloud generation. In particular, our DiT-3D decreases the 1-Nearest Neighbor Accuracy of the state-of-the-art method by 4.59 and increases the Coverage metric by 3.51 when evaluated on Chamfer Distance.},
  journal = {arXiv (Cornell University)},
  year = {2023},
  month = {01},
  doi = {10.48550/arxiv.2307.01831},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/2307.01831},
  publisher = {Cornell University}
}
@article{nico_engel_ec68fdbc,
  author = {Nico Engel AND Vasileios Belagiannis AND Klaus Dietmayer},
  title = {Point Transformer},
  abstract = {In this work, we present Point Transformer, a deep neural network that operates directly on unordered and unstructured point sets. We design Point Transformer to extract local and global features and relate both representations by introducing the local-global attention mechanism, which aims to capture spatial point relations and shape information. For that purpose, we propose SortNet, as part of the Point Transformer, which induces input permutation invariance by selecting points based on a learned score. The output of Point Transformer is a sorted and permutation invariant feature list that can directly be incorporated into common computer vision applications. We evaluate our approach on standard classification and part segmentation benchmarks to demonstrate competitive results compared to the prior work.},
  journal = {IEEE Access},
  volume = {9},
  pages = {134826-134840},
  year = {2021},
  month = {01},
  doi = {10.1109/access.2021.3116304},
  issn = {2169-3536},
  isbn = {},
  url = {https://doi.org/10.1109/access.2021.3116304},
  publisher = {Institute of Electrical and Electronics Engineers}
}
@article{meng_hao_guo_d63ca865,
  author = {Meng-Hao Guo AND Jun-Xiong Cai AND Zheng-Ning Liu AND Tai‐Jiang Mu AND Ralph R. Martin AND Shi‐Min Hu},
  title = {PCT: Point cloud transformer},
  abstract = {The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named Point Cloud Transformer(PCT) for point cloud learning. PCT is based on Transformer, which achieves huge success in natural language processing and displays great potential in image processing. It is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning. To better capture local context within the point cloud, we enhance input embedding with the support of farthest point sampling and nearest neighbor search. Extensive experiments demonstrate that the PCT achieves the state-of-the-art performance on shape classification, part segmentation and normal estimation tasks.},
  journal = {Computational Visual Media},
  volume = {7},
  issue = {2},
  pages = {187-199},
  year = {2021},
  month = {04},
  doi = {10.1007/s41095-021-0229-5},
  issn = {2096-0433, 2096-0662},
  isbn = {},
  url = {https://doi.org/10.1007/s41095-021-0229-5},
  publisher = {Springer Nature}
}
@inproceedings{xingguang_yan_74ea09da,
  author = {Xingguang Yan AND Liqiang Lin AND Niloy J. Mitra AND Dani Lischinski AND Daniel Cohen-Or AND Hui Huang},
  title = {ShapeFormer: Transformer-Based Shape Completion via Sparse Representation},
  abstract = {We present ShapeFormer, a transformer-based network that produces a distribution of object completions, conditioned on incomplete, and possibly noisy, point clouds. The resultant distribution can then be sampled to generate likely completions, each exhibiting plausible shape details while being faithful to the input. To facilitate the use of transformers for 3D, we introduce a compact 3D representation, vector quantized deep implicit function (VQDIF), that utilizes spatial sparsity to represent a close approximation of a 3D shape by a short sequence of discrete variables. Experiments demonstrate that ShapeFormer outperforms prior art for shape completion from ambiguous partial inputs in terms of both completion quality and diversity. We also show that our approach effectively handles a variety of shape types, incomplete patterns, and real-world scans.},
  pages = {6239-6249},
  year = {2022},
  month = {01},
  doi = {10.1109/cvpr52688.2022.00614},
  issn = {},
  isbn = {},
  url = {https://openaccess.thecvf.com/content/CVPR2022/html/Yan_ShapeFormer_Transformer-Based_Shape_Completion_via_Sparse_Representation_CVPR_2022_paper.html},
  shorttitle = {ShapeFormer},
  language = {en},
  file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/TGZULQDL/Yan et al. - 2022 - ShapeFormer Transformer-Based Shape Completion vi.pdf:application/pdf},
  urldate = {2023-09-06}
}
@misc{biao_zhang_c355a406,
  author = {Biao Zhang AND Jiapeng Tang AND Matthias Niessner AND Peter Wonka},
  title = {3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models},
  abstract = {We introduce 3DShape2VecSet, a novel shape representation for neural fields designed for generative diffusion models. Our shape representation can encode 3D shapes given as surface models or point clouds, and represents them as neural fields. The concept of neural fields has previously been combined with a global latent vector, a regular grid of latent vectors, or an irregular grid of latent vectors. Our new representation encodes neural fields on top of a set of vectors. We draw from multiple concepts, such as the radial basis function representation and the cross attention and self-attention function, to design a learnable representation that is especially suitable for processing with transformers. Our results show improved performance in 3D shape encoding and 3D shape generative modeling tasks. We demonstrate a wide variety of generative applications: unconditioned generation, category-conditioned generation, text-conditioned generation, point-cloud completion, and image-conditioned generation.},
  year = {2023},
  month = {05},
  doi = {10.48550/arXiv.2301.11445},
  issn = {},
  isbn = {},
  url = {http://arxiv.org/abs/2301.11445},
  publisher = {arXiv},
  shorttitle = {3DShape2VecSet},
  urldate = {2024-12-30},
  keywords = {Computer Science - Computer Vision and Pattern Recognition},
  note = {arXiv:2301.11445 [cs]},
  file = {Preprint PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/9VHMHUW7/Zhang et al. - 2023 - 3DShape2VecSet A 3D Shape Representation for Neural Fields and Generative Diffusion Models.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/FND23JG7/2301.html:text/html}
}
@online{kangle_deng_f2a880c2,
  author = {Kangle Deng AND Hsueh-Ti Derek Liu AND Yiheng Zhu AND Xiaoxia Sun AND Chong Shang AND Kiran S. Bhat AND Deva Ramanan AND Jun-Yan Zhu AND Maneesh Agrawala AND Tinghui Zhou},
  title = {Efficient Autoregressive Shape Generation via Octree-Based Adaptive Tokenization},
  year = {2025},
  month = {04},
  issn = {},
  isbn = {}
}
@misc{moritz_ibing_7d77f799,
  author = {Moritz Ibing AND Gregor Kobsik AND Leif Kobbelt},
  title = {Octree Transformer: Autoregressive 3D Shape Generation on Hierarchically Structured Sequences},
  abstract = {Autoregressive models have proven to be very powerful in NLP text generation tasks and lately have gained popularity for image generation as well. However, they have seen limited use for the synthesis of 3D shapes so far. This is mainly due to the lack of a straightforward way to linearize 3D data as well as to scaling problems with the length of the resulting sequences when describing complex shapes. In this work we address both of these problems. We use octrees as a compact hierarchical shape representation that can be sequentialized by traversal ordering. Moreover, we introduce an adaptive compression scheme, that significantly reduces sequence lengths and thus enables their effective generation with a transformer, while still allowing fully autoregressive sampling and parallel training. We demonstrate the performance of our model by comparing against the state-of-the-art in shape generation.},
  year = {2021},
  month = {11},
  doi = {10.48550/arXiv.2111.12480},
  issn = {},
  isbn = {},
  url = {http://arxiv.org/abs/2111.12480},
  publisher = {arXiv},
  keywords = {Computer Science - Machine Learning},
  urldate = {2023-09-06},
  note = {arXiv:2111.12480 [cs]},
  shorttitle = {Octree Transformer},
  file = {arXiv Fulltext PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/SFCGQSIL/Ibing et al. - 2021 - Octree Transformer Autoregressive 3D Shape Genera.pdf:application/pdf;arXiv.org Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/KXDYY3TK/2111.html:text/html}
}
@article{chao_zhou_6031ed06,
  author = {Chao Zhou AND Yanan Zhang AND Jiaxin Chen AND Di Huang},
  title = {OcTr: Octree-based Transformer for 3D Object Detection},
  abstract = {A key challenge for LiDAR-based 3D object detection is to capture sufficient features from large scale 3D scenes especially for distant or/and occluded objects. Albeit recent efforts made by Transformers with the long sequence modeling capability, they fail to properly balance the accuracy and efficiency, suffering from inadequate receptive fields or coarse-grained holistic correlations. In this paper, we propose an Octree-based Transformer, named OcTr, to address this issue. It first constructs a dynamic octree on the hierarchical feature pyramid through conducting self-attention on the top level and then recursively propagates to the level below restricted by the octants, which captures rich global context in a coarse-to-fine manner while maintaining the computational complexity under control. Furthermore, for enhanced foreground perception, we propose a hybrid positional embedding, composed of the semantic-aware positional embedding and attention mask, to fully exploit semantic and geometry clues. Extensive experiments are conducted on the Waymo Open Dataset and KITTI Dataset, and OcTr reaches newly state-of-the-art results.},
  journal = {arXiv (Cornell University)},
  year = {2023},
  month = {01},
  doi = {10.48550/arxiv.2303.12621},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/2303.12621},
  publisher = {Cornell University}
}
@inproceedings{chunyang_fu_5753cfea,
  author = {Chunyang Fu AND Ge Li AND Rui Song AND Wei Gao AND Shan Liu},
  title = {OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression},
  abstract = {In point cloud compression, sufficient contexts are significant for modeling the point cloud distribution. However, the contexts gathered by the previous voxel-based methods decrease when handling sparse point clouds. To address this problem, we propose a multiple-contexts deep learning framework called OctAttention employing the octree structure, a memory-efficient representation for point clouds. Our approach encodes octree symbol sequences in a lossless way by gathering the information of sibling and ancestor nodes. Expressly, we first represent point clouds with octree to reduce spatial redundancy, which is robust for point clouds with different resolutions. We then design a conditional entropy model with a large receptive field that models the sibling and ancestor contexts to exploit the strong dependency among the neighboring nodes and employ an attention mechanism to emphasize the correlated nodes in the context. Furthermore, we introduce a mask operation during training and testing to make a trade-off between encoding time and performance. Compared to the previous state-of-the-art works, our approach obtains a 10%-35% BD-Rate gain on the LiDAR benchmark (e.g. SemanticKITTI) and object point cloud dataset (e.g. MPEG 8i, MVUB), and saves 95% coding time compared to the voxel-based baseline. The code is available at https://github.com/zb12138/OctAttention.},
  journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
  volume = {36},
  issue = {1},
  pages = {625-633},
  year = {2022},
  month = {06},
  doi = {10.1609/aaai.v36i1.19942},
  issn = {2159-5399, 2374-3468},
  isbn = {},
  url = {https://doi.org/10.1609/aaai.v36i1.19942},
  publisher = {Association for the Advancement of Artificial Intelligence}
}
@misc{si_tong_wei_34b5adf6,
  author = {Si-Tong Wei AND Rui-Huan Wang AND Chuan-Zhi Zhou AND Baoquan Chen AND Peng-Shuai Wang},
  title = {OctGPT: Octree-based Multiscale Autoregressive Models for 3D Shape Generation},
  abstract = {Autoregressive models have achieved remarkable success across various domains, yet their performance in 3D shape generation lags significantly behind that of diffusion models. In this paper, we introduce OctGPT, a novel multiscale autoregressive model for 3D shape generation that dramatically improves the efficiency and performance of prior 3D autoregressive approaches, while rivaling or surpassing state-of-the-art diffusion models. Our method employs a serialized octree representation to efficiently capture the hierarchical and spatial structures of 3D shapes. Coarse geometry is encoded via octree structures, while fine-grained details are represented by binary tokens generated using a vector quantized variational autoencoder (VQVAE), transforming 3D shapes into compact multiscale binary sequences suitable for autoregressive prediction. To address the computational challenges of handling long sequences, we incorporate octree-based transformers enhanced with 3D rotary positional encodings, scale-specific embeddings, and token-parallel generation schemes. These innovations reduce training time by 13 folds and generation time by 69 folds, enabling the efficient training of high-resolution 3D shapes, e.g.,$1024^3$, on just four NVIDIA 4090 GPUs only within days. OctGPT showcases exceptional versatility across various tasks, including text-, sketch-, and image-conditioned generation, as well as scene-level synthesis involving multiple objects. Extensive experiments demonstrate that OctGPT accelerates convergence and improves generation quality over prior autoregressive methods, offering a new paradigm for high-quality, scalable 3D content creation. Our code and trained models are available at https://github.com/octree-nn/octgpt.},
  year = {2025},
  month = {04},
  doi = {10.48550/arXiv.2504.09975},
  issn = {},
  isbn = {},
  url = {http://arxiv.org/abs/2504.09975},
  publisher = {arXiv},
  urldate = {2025-04-24},
  file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/LV6XVAGF/Wei et al. - 2025 - OctGPT Octree-based Multiscale Autoregressive Models for 3D Shape Generation.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/T7IJJ8JN/2504.html:text/html},
  keywords = {Computer Science - Computer Vision and Pattern Recognition},
  shorttitle = {OctGPT},
  note = {arXiv:2504.09975 [cs]}
}
@article{peng_shuai_wang_6465e142,
  author = {Peng-Shuai Wang},
  title = {OctFormer: Octree-based Transformers for 3D Point Clouds},
  abstract = {We propose octree-based transformers, named OctFormer, for 3D point cloud learning. OctFormer can not only serve as a general and effective backbone for 3D point cloud segmentation and object detection but also have linear complexity and is scalable for large-scale point clouds. The key challenge in applying transformers to point clouds is reducing the quadratic, thus overwhelming, computation complexity of attentions. To combat this issue, several works divide point clouds into non-overlapping windows and constrain attentions in each local window. However, the point number in each window varies greatly, impeding the efficient execution on GPU. Observing that attentions are robust to the shapes of local windows, we propose a novel octree attention, which leverages sorted shuffled keys of octrees to partition point clouds into local windows containing a fixed number of points while permitting shapes of windows to change freely. And we also introduce dilated octree attention to expand the receptive field further. Our octree attention can be implemented in 10 lines of code with open-sourced libraries and runs 17 times faster than other point cloud attentions when the point number exceeds 200 k. Built upon the octree attention, OctFormer can be easily scaled up and achieves state-of-the-art performances on a series of 3D semantic segmentation and 3D object detection benchmarks, surpassing previous sparse-voxel-based CNNs and point cloud transformers in terms of both efficiency and effectiveness. Notably, on the challenging ScanNet200 dataset, OctFormer outperforms sparse-voxel-based CNNs by 7.3 in mIoU. Our code and trained models are available at https://wang-ps.github.io/octformer.},
  journal = {ACM Trans. Graph.},
  volume = {42},
  number = {4},
  pages = {1-11},
  year = {2023},
  month = {08},
  doi = {10.1145/3592131},
  issn = {0730-0301, 1557-7368},
  isbn = {},
  url = {http://arxiv.org/abs/2305.03045},
  publisher = {Association for Computing Machinery},
  urldate = {2025-04-24},
  note = {arXiv:2305.03045 [cs]},
  shorttitle = {OctFormer},
  file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/6NMDWNSI/Wang - 2023 - OctFormer Octree-based Transformers for 3D Point Clouds.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/MQSF26S6/2305.html:text/html},
  keywords = {Computer Science - Computer Vision and Pattern Recognition}
}
@article{rui_chen_60bda65e,
  author = {Rui Chen AND Jianfeng Zhang AND Yixun Liang AND Luo Guan AND Weiyu Li AND Jiarui Liu AND Xiu Li AND Xiaoxiao Long AND Jiashi Feng AND Ping Tan},
  title = {Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders},
  abstract = {Recent 3D content generation pipelines commonly employ Variational Autoencoders (VAEs) to encode shapes into compact latent representations for diffusion-based generation. However, the widely adopted uniform point sampling strategy in Shape VAE training often leads to a significant loss of geometric details, limiting the quality of shape reconstruction and downstream generation tasks. We present Dora-VAE, a novel approach that enhances VAE reconstruction through our proposed sharp edge sampling strategy and a dual cross-attention mechanism. By identifying and prioritizing regions with high geometric complexity during training, our method significantly improves the preservation of fine-grained shape features. Such sampling strategy and the dual attention mechanism enable the VAE to focus on crucial geometric details that are typically missed by uniform sampling approaches. To systematically evaluate VAE reconstruction quality, we additionally propose Dora-bench, a benchmark that quantifies shape complexity through the density of sharp edges, introducing a new metric focused on reconstruction accuracy at these salient geometric features. Extensive experiments on the Dora-bench demonstrate that Dora-VAE achieves comparable reconstruction quality to the state-of-the-art dense XCube-VAE while requiring a latent space at least 8$\times$ smaller (1,280 vs. > 10,000 codes). We will release our code and benchmark dataset to facilitate future research in 3D shape modeling.},
  journal = {arXiv (Cornell University)},
  year = {2024},
  month = {12},
  doi = {10.48550/arxiv.2412.17808},
  issn = {},
  isbn = {},
  url = {http://arxiv.org/abs/2412.17808},
  publisher = {Cornell University}
}

@misc{prashanth_chandran_5f194572,
  author = {Prashanth Chandran AND Gaspard Zoss AND Markus Groß AND Paulo Gotardo AND Derek Bradley},
  title = {Shape Transformers: Topology‐Independent 3D Shape Models Using Transformers},
  abstract = {Abstract Parametric 3D shape models are heavily utilized in computer graphics and vision applications to provide priors on the observed variability of an object's geometry ( e.g ., for faces). Original models were linear and operated on the entire shape at once. They were later enhanced to provide localized control on different shape parts separately. In deep shape models, nonlinearity was introduced via a sequence of fully‐connected layers and activation functions, and locality was introduced in recent models that use mesh convolution networks. As common limitations, these models often dictate, in one way or another, the allowed extent of spatial correlations and also require that a fixed mesh topology be specified ahead of time. To overcome these limitations, we present Shape Transformers , a new nonlinear parametric 3D shape model based on transformer architectures. A key benefit of this new model comes from using the transformer's self‐attention mechanism to automatically learn nonlinear spatial correlations for a class of 3D shapes. This is in contrast to global models that correlate everything and local models that dictate the correlation extent. Our transformer 3D shape autoencoder is a better alternative to mesh convolution models, which require specially‐crafted convolution, and down/up‐sampling operators that can be difficult to design. Our model is also topologically independent: it can be trained once and then evaluated on any mesh topology, unlike most previous methods. We demonstrate the application of our model to different datasets, including 3D faces, 3D hand shapes and full human bodies. Our experiments demonstrate the strong potential of our Shape Transformer model in several applications in computer graphics and vision.},
  pages = {195-207},
  year = {2022},
  month = {05},
  doi = {10.1111/cgf.14468},
  issn = {},
  isbn = {},
  url = {https://onlinelibrary.wiley.com/doi/epdf/10.1111/cgf.14468},
  publisher = {Wiley},
  file = {Shape Transformers\: Topology‐Independent 3D Shape Models Using Transformers:/home/localaak22/snap/zotero-snap/common/Zotero/storage/3IEGXUH2/cgf.html:text/html},
  urldate = {2024-11-01}
}
@inproceedings{yusuke_yoshiyasu_be8961bd,
  author = {Yusuke Yoshiyasu},
  title = {Deformable Mesh Transformer for 3D Human Mesh Recovery},
  abstract = {We present Deformable mesh transFormer (DeFormer), a novel vertex-based approach to monocular 3D human mesh recovery. DeFormer iteratively fits a body mesh model to an input image via a mesh alignment feedback loop formed within a transformer decoder that is equipped with efficient body mesh driven attention modules: 1) body sparse self-attention and 2) deformable mesh cross attention. As a result, DeFormer can effectively exploit high-resolution image feature maps and a dense mesh model which were computationally expensive to deal with in previous approaches using the standard transformer attention. Experimental results show that DeFormer achieves state-of-the-art performances on the Human3.6M and 3DPW benchmarks. Ablation study is also conducted to show the effectiveness of the DeFormer model designs for leveraging multi-scale feature maps. Code is available at https://github.com/yusukey03012/DeFormer.},
  pages = {17006-17015},
  year = {2023},
  month = {06},
  doi = {10.1109/CVPR52729.2023.01631},
  issn = {},
  isbn = {979-8-3503-0129-8},
  url = {https://ieeexplore.ieee.org/document/10204623/},
  publisher = {IEEE},
  address = {Vancouver, BC, Canada},
  copyright = {https://doi.org/10.15223/policy-029},
  language = {en},
  booktitle = {2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  file = {PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/QLC776K5/Yoshiyasu - 2023 - Deformable Mesh Transformer for 3D Human Mesh Recovery.pdf:application/pdf},
  urldate = {2025-05-13}
}
@article{yuan_li_6a781f36,
  author = {Yuan Li AND Xiangyang He AND Yankai Jiang AND Huan Liu AND Yubo Tao AND Hai Lin},
  title = {MeshFormer: High‐resolution Mesh Segmentation with Graph Transformer},
  abstract = {Abstract Graph transformer has achieved remarkable success in graph‐based segmentation tasks. Inspired by this success, we propose a novel method named MeshFormer for applying the graph transformer to the semantic segmentation of high‐resolution meshes. The main challenges are the large data size, the massive model size, and the insufficient extraction of high‐resolution semantic meanings. The large data or model size necessitates unacceptably extensive computational resources, and the insufficient semantic meanings lead to inaccurate segmentation results. MeshFormer addresses these three challenges with three components. First, a boundary‐preserving simplification is introduced to reduce the data size while maintaining the critical high‐resolution information in segmentation boundaries. Second, a Ricci flow‐based clustering algorithm is presented for constructing hierarchical structures of meshes, replacing many convolutions layers for global support with only a few convolutions in hierarchy structures. In this way, the model size can be reduced to an acceptable range. Third, we design a graph transformer with cross‐resolution convolutions, which extracts richer high‐resolution semantic meanings and improves segmentation results over previous methods. Experiments show that MeshFormer achieves gains from 1.0% to 5.8% on artificial and real‐world datasets.},
  journal = {Computer Graphics Forum},
  volume = {41},
  issue = {7},
  pages = {37-49},
  year = {2022},
  month = {10},
  doi = {10.1111/cgf.14655},
  issn = {0167-7055, 1467-8659},
  isbn = {},
  url = {https://doi.org/10.1111/cgf.14655},
  publisher = {Wiley}
}
@article{yawar_siddiqui_201cf948,
  author = {Yawar Siddiqui AND Antonio Alliegro AND Alexey Artemov AND Tatiana Tommasi AND Daniele Sirigatti AND Vladislav Rosov AND Angela Dai AND Matthias Nießner},
  title = {MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers},
  abstract = {We introduce MeshGPT, a new approach for generating triangle meshes that reflects the compactness typical of artist-created meshes, in contrast to dense triangle meshes extracted by iso-surfacing methods from neural fields. Inspired by recent advances in powerful large language models, we adopt a sequence-based approach to autoregressively generate triangle meshes as sequences of triangles. We first learn a vocabulary of latent quantized embeddings, using graph convolutions, which inform these embeddings of the local mesh geometry and topology. These embeddings are sequenced and decoded into triangles by a decoder, ensuring that they can effectively reconstruct the mesh. A transformer is then trained on this learned vocabulary to predict the index of the next embedding given previous embeddings. Once trained, our model can be autoregressively sampled to generate new triangle meshes, directly generating compact meshes with sharp edges, more closely imitating the efficient triangulation patterns of human-crafted meshes. MeshGPT demonstrates a notable improvement over state of the art mesh generation methods, with a 9% increase in shape coverage and a 30-point enhancement in FID scores across various categories.},
  journal = {arXiv (Cornell University)},
  year = {2023},
  month = {01},
  doi = {10.48550/arxiv.2311.15475},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/2311.15475},
  publisher = {Cornell University}
}
@online{jian_liu_b3fa4d91,
  author = {Jian Liu AND Haohan Weng AND Biwen Lei AND Xianghui Yang AND Zibo Zhao AND Zhuo Chen AND Song Guo AND Tao Han AND Chunchao Guo},
  title = {FreeMesh: Boosting Mesh Generation with Coordinates Merging},
  year = {2025},
  month = {05},
  issn = {},
  isbn = {}
}
@article{jiaxiang_tang_16874ec0,
  author = {Jiaxiang Tang AND Zhaoshuo Li AND Zekun Hao AND Xian Liu AND Gang Zeng AND Ming-Yu Liu AND Qinsheng Zhang},
  title = {EdgeRunner: Auto-regressive Auto-encoder for Artistic Mesh Generation},
  abstract = {Current auto-regressive mesh generation methods suffer from issues such as incompleteness, insufficient detail, and poor generalization. In this paper, we propose an Auto-regressive Auto-encoder (ArAE) model capable of generating high-quality 3D meshes with up to 4,000 faces at a spatial resolution of $512^3$. We introduce a novel mesh tokenization algorithm that efficiently compresses triangular meshes into 1D token sequences, significantly enhancing training efficiency. Furthermore, our model compresses variable-length triangular meshes into a fixed-length latent space, enabling training latent diffusion models for better generalization. Extensive experiments demonstrate the superior quality, diversity, and generalization capabilities of our model in both point cloud and image-conditioned mesh generation tasks.},
  journal = {arXiv (Cornell University)},
  year = {2024},
  month = {09},
  doi = {10.48550/arxiv.2409.18114},
  issn = {},
  isbn = {},
  url = {http://arxiv.org/abs/2409.18114},
  publisher = {Cornell University}
}
@misc{haoxuan_li_575772bf,
  author = {Haoxuan Li AND Ziya Erkoc AND Lei Li AND Daniele Sirigatti AND Vladyslav Rozov AND Angela Dai AND Matthias Nießner},
  title = {MeshPad: Interactive Sketch-Conditioned Artist-Designed Mesh Generation and Editing},
  abstract = {We introduce MeshPad, a generative approach that creates 3D meshes from sketch inputs. Building on recent advances in artist-designed triangle mesh generation, our approach addresses the need for interactive mesh creation. To this end, we focus on enabling consistent edits by decomposing editing into 'deletion' of regions of a mesh, followed by 'addition' of new mesh geometry. Both operations are invoked by simple user edits of a sketch image, facilitating an iterative content creation process and enabling the construction of complex 3D meshes. Our approach is based on a triangle sequence-based mesh representation, exploiting a large Transformer model for mesh triangle addition and deletion. In order to perform edits interactively, we introduce a vertex-aligned speculative prediction strategy on top of our additive mesh generator. This speculator predicts multiple output tokens corresponding to a vertex, thus significantly reducing the computational cost of inference and accelerating the editing process, making it possible to execute each editing step in only a few seconds. Comprehensive experiments demonstrate that MeshPad outperforms state-of-the-art sketch-conditioned mesh generation methods, achieving more than 22% mesh quality improvement in Chamfer distance, and being preferred by 90% of participants in perceptual evaluations.},
  year = {2025},
  month = {03},
  doi = {10.48550/arXiv.2503.01425},
  issn = {},
  isbn = {},
  url = {http://arxiv.org/abs/2503.01425},
  publisher = {arXiv},
  file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/HJTTEDU2/Li et al. - 2025 - MeshPad Interactive Sketch-Conditioned Artist-Designed Mesh Generation and Editing.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/NASSPYYN/2503.html:text/html},
  note = {arXiv:2503.01425 [cs]},
  urldate = {2025-04-04},
  shorttitle = {MeshPad},
  keywords = {Computer Science - Computer Vision and Pattern Recognition}
}
@article{yiwen_chen_e916216d,
  author = {Yiwen Chen AND Tong He AND Di Huang AND Weicai Ye AND Sijin Chen AND Jiaxiang Tang AND Xin Chen AND Zhongang Cai AND Lei Yang AND Gang Yu AND Guosheng Lin AND Chi Zhang},
  title = {MeshAnything: Artist-Created Mesh Generation with Autoregressive
  Transformers},
  abstract = {Recently, 3D assets created via reconstruction and generation have matched the quality of manually crafted assets, highlighting their potential for replacement. However, this potential is largely unrealized because these assets always need to be converted to meshes for 3D industry applications, and the meshes produced by current mesh extraction methods are significantly inferior to Artist-Created Meshes (AMs), i.e., meshes created by human artists. Specifically, current mesh extraction methods rely on dense faces and ignore geometric features, leading to inefficiencies, complicated post-processing, and lower representation quality. To address these issues, we introduce MeshAnything, a model that treats mesh extraction as a generation problem, producing AMs aligned with specified shapes. By converting 3D assets in any 3D representation into AMs, MeshAnything can be integrated with various 3D asset production methods, thereby enhancing their application across the 3D industry. The architecture of MeshAnything comprises a VQ-VAE and a shape-conditioned decoder-only transformer. We first learn a mesh vocabulary using the VQ-VAE, then train the shape-conditioned decoder-only transformer on this vocabulary for shape-conditioned autoregressive mesh generation. Our extensive experiments show that our method generates AMs with hundreds of times fewer faces, significantly improving storage, rendering, and simulation efficiencies, while achieving precision comparable to previous methods.},
  journal = {arXiv (Cornell University)},
  year = {2024},
  month = {06},
  doi = {10.48550/arxiv.2406.10163},
  issn = {},
  isbn = {},
  url = {http://arxiv.org/abs/2406.10163},
  publisher = {Cornell University}
}
@article{sijin_chen_c6291a90,
  author = {Sijin Chen AND Xin Chen AND Anqi Pang AND Xianfang Zeng AND Cheng Wei AND Yijun Fu AND Fukun Yin AND Yanru Wang AND Zhibin Wang AND Chi Zhang AND Jingyi Yu AND Gang Yu AND Bin Fu AND Tao Chen},
  title = {MeshXL: Neural Coordinate Field for Generative 3D Foundation Models},
  abstract = {The polygon mesh representation of 3D data exhibits great flexibility, fast rendering speed, and storage efficiency, which is widely preferred in various applications. However, given its unstructured graph representation, the direct generation of high-fidelity 3D meshes is challenging. Fortunately, with a pre-defined ordering strategy, 3D meshes can be represented as sequences, and the generation process can be seamlessly treated as an auto-regressive problem. In this paper, we validate the Neural Coordinate Field (NeurCF), an explicit coordinate representation with implicit neural embeddings, is a simple-yet-effective representation for large-scale sequential mesh modeling. After that, we present MeshXL, a family of generative pre-trained auto-regressive models, which addresses the process of 3D mesh generation with modern large language model approaches. Extensive experiments show that MeshXL is able to generate high-quality 3D meshes, and can also serve as foundation models for various down-stream applications.},
  journal = {arXiv (Cornell University)},
  year = {2024},
  month = {05},
  doi = {10.48550/arxiv.2405.20853},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/2405.20853},
  publisher = {Cornell University}
}
@misc{zekun_hao_ecc73d1a,
  author = {Zekun Hao AND David W. Romero AND Tsung-Yi Lin AND Ming-Yu Liu},
  title = {Meshtron: High-Fidelity, Artist-Like 3D Mesh Generation at Scale},
  abstract = {Meshes are fundamental representations of 3D surfaces. However, creating high-quality meshes is a labor-intensive task that requires significant time and expertise in 3D modeling. While a delicate object often requires over $10^4$ faces to be accurately modeled, recent attempts at generating artist-like meshes are limited to $1.6$K faces and heavy discretization of vertex coordinates. Hence, scaling both the maximum face count and vertex coordinate resolution is crucial to producing high-quality meshes of realistic, complex 3D objects. We present Meshtron, a novel autoregressive mesh generation model able to generate meshes with up to 64K faces at 1024-level coordinate resolution --over an order of magnitude higher face count and $8{\times}$ higher coordinate resolution than current state-of-the-art methods. Meshtron's scalability is driven by four key components: (1) an hourglass neural architecture, (2) truncated sequence training, (3) sliding window inference, (4) a robust sampling strategy that enforces the order of mesh sequences. This results in over $50{\%}$ less training memory, $2.5{\times}$ faster throughput, and better consistency than existing works. Meshtron generates meshes of detailed, complex 3D objects at unprecedented levels of resolution and fidelity, closely resembling those created by professional artists, and opening the door to more realistic generation of detailed 3D assets for animation, gaming, and virtual environments.},
  year = {2024},
  month = {12},
  doi = {10.48550/arXiv.2412.09548},
  issn = {},
  isbn = {},
  url = {http://arxiv.org/abs/2412.09548},
  publisher = {arXiv},
  file = {Preprint PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/473DTXW6/Hao et al. - 2024 - Meshtron High-Fidelity, Artist-Like 3D Mesh Generation at Scale.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/BH433LVJ/2412.html:text/html},
  keywords = {Computer Science - Computer Vision and Pattern Recognition},
  shorttitle = {Meshtron},
  note = {arXiv:2412.09548 [cs]},
  urldate = {2025-02-14}
}
@misc{yi_ling_qiao_04b587d6,
  author = {Yi-Ling Qiao AND Lin Gao AND Jie Yang AND Paul L. Rosin AND Yu-Kun Lai AND Xilin Chen},
  title = {LaplacianNet: Learning on 3D Meshes with Laplacian Encoding and Pooling},
  abstract = {3D models are commonly used in computer vision and graphics. With the wider availability of mesh data, an efficient and intrinsic deep learning approach to processing 3D meshes is in great need. Unlike images, 3D meshes have irregular connectivity, requiring careful design to capture relations in the data. To utilize the topology information while staying robust under different triangulation, we propose to encode mesh connectivity using Laplacian spectral analysis, along with Mesh Pooling Blocks (MPBs) that can split the surface domain into local pooling patches and aggregate global information among them. We build a mesh hierarchy from fine to coarse using Laplacian spectral clustering, which is flexible under isometric transformation. Inside the MPBs there are pooling layers to collect local information and multi-layer perceptrons to compute vertex features with increasing complexity. To obtain the relationships among different clusters, we introduce a Correlation Net to compute a correlation matrix, which can aggregate the features globally by matrix multiplication with cluster features. Our network architecture is flexible enough to be used on meshes with different numbers of vertices. We conduct several experiments including shape segmentation and classification, and our LaplacianNet outperforms state-of-the-art algorithms for these tasks on ShapeNet and COSEG datasets.},
  year = {2019},
  month = {10},
  doi = {10.48550/arXiv.1910.14063},
  issn = {},
  isbn = {},
  url = {http://arxiv.org/abs/1910.14063},
  publisher = {arXiv},
  shorttitle = {LaplacianNet},
  urldate = {2024-12-12},
  file = {Preprint PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/SYKITJQS/Qiao et al. - 2019 - LaplacianNet Learning on 3D Meshes with Laplacian Encoding and Pooling.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/9D4TKPMX/1910.html:text/html},
  keywords = {Computer Science - Computer Vision and Pattern Recognition},
  note = {arXiv:1910.14063 [cs]}
}
@article{qiujie_dong_a857e06e,
  author = {Qiujie Dong AND Zixiong Wang AND Manyi Li AND Junjie Gao AND Shuangmin Chen AND Zhenyu Shu AND Shiqing Xin AND Changhe Tu AND Wenping Wang},
  title = {Laplacian2Mesh: Laplacian-Based Mesh Understanding},
  abstract = {Geometric deep learning has sparked a rising interest in computer graphics to perform shape understanding tasks, such as shape classification and semantic segmentation. When the input is a polygonal surface, one has to suffer from the irregular mesh structure. Motivated by the geometric spectral theory, we introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Laplacian2Mesh</i> , a novel and flexible convolutional neural network (CNN) framework for coping with irregular triangle meshes (vertices may have any valence). By mapping the input mesh surface to the multi-dimensional Laplacian-Beltrami space, Laplacian2Mesh enables one to perform shape analysis tasks directly using the mature CNNs, without the need to deal with the irregular connectivity of the mesh structure. We further define a mesh pooling operation such that the receptive field of the network can be expanded while retaining the original vertex set as well as the connections between them. Besides, we introduce a channel-wise self-attention block to learn the individual importance of feature ingredients. Laplacian2Mesh not only decouples the geometry from the irregular connectivity of the mesh structure but also better captures the global features that are central to shape classification and segmentation. Extensive tests on various datasets demonstrate the effectiveness and efficiency of Laplacian2Mesh, particularly in terms of the capability of being vulnerable to noise to fulfill various learning tasks.},
  journal = {IEEE Trans. Visual. Comput. Graphics},
  volume = {30},
  number = {7},
  pages = {4349-4361},
  year = {2024},
  month = {07},
  doi = {10.1109/TVCG.2023.3259044},
  pmid = {37030768},
  issn = {1077-2626, 1941-0506, 2160-9306},
  isbn = {},
  url = {http://arxiv.org/abs/2202.00307},
  publisher = {Institute of Electrical and Electronics Engineers},
  keywords = {Computer Science - Machine Learning},
  shorttitle = {Laplacian2Mesh},
  file = {Preprint PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/3U2PHWUT/Dong et al. - 2024 - Laplacian2Mesh Laplacian-Based Mesh Understanding.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/JAJ3ZXT2/2202.html:text/html},
  urldate = {2024-12-12},
  note = {arXiv:2202.00307 [cs]}
}
@article{herv__lombaert_d30fbcc9,
  author = {Hervé Lombaert AND Michael Arcaro AND Nicholas Ayache},
  title = {Brain Transfer: Spectral Analysis of Cortical Surfaces and Functional Maps},
  journal = {Lecture notes in computer science},
  pages = {474-487},
  year = {2015},
  month = {01},
  doi = {10.1007/978-3-319-19992-4_37},
  pmid = {26221696},
  issn = {0302-9743, 1611-3349},
  isbn = {},
  url = {https://doi.org/10.1007/978-3-319-19992-4_37},
  publisher = {Springer Science+Business Media}
}
@inproceedings{ran_he_1ac27b92,
  author = {Ran He AND Karthik Gopinath AND Christian Desrosiers AND Hervé Lombaert},
  title = {Spectral Graph Transformer Networks for Brain Surface Parcellation},
  abstract = {The analysis of the brain surface modeled as a graph mesh is a challenging task. Conventional deep learning approaches often rely on data lying in the Euclidean space. As an extension to irregular graphs, convolution operations are defined in the Fourier or spectral domain. This spectral domain is obtained by decomposing the graph Laplacian, which captures relevant shape information. However, the spectral decomposition across different brain graphs causes inconsistencies between the eigenvectors of individual spectral domains, causing the graph learning algorithm to fail. Current spectral graph convolution methods handle this variance by separately aligning the eigenvectors to a reference brain in a slow iterative step. This paper presents a novel approach for learning the transformation matrix required for aligning brain meshes using a direct data-driven approach. Our alignment and graph processing method provides a fast analysis of brain surfaces. The novel Spectral Graph Transformer (SGT) network proposed in this paper uses very few randomly sub-sampled nodes in the spectral domain to learn the alignment matrix for multiple brain surfaces. We validate the use of this SGT network along with a graph convolution network to perform cortical parcellation. Our method on 101 manually-labeled brain surfaces shows improved parcellation performance over a no-alignment strategy, gaining a significant speed (1400 fold) over traditional iterative alignment approaches.},
  journal = {2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)},
  year = {2020},
  month = {04},
  doi = {10.1109/isbi45749.2020.9098737},
  issn = {},
  isbn = {},
  url = {https://doi.org/10.1109/isbi45749.2020.9098737}
}
@article{ivan_anokhin_abbd2f6b,
  author = {Ivan Anokhin AND Kirill Demochkin AND Taras Khakhulin AND Gleb Sterkin AND Victor Lempitsky AND Denis Korzhenkov},
  title = {Image Generators with Conditionally-Independent Pixel Synthesis},
  abstract = {Existing image generator networks rely heavily on spatial convolutions and, optionally, self-attention blocks in order to gradually synthesize images in a coarse-to-fine manner. Here, we present a new architecture for image generators, where the color value at each pixel is computed independently given the value of a random latent vector and the coordinate of that pixel. No spatial convolutions or similar operations that propagate information across pixels are involved during the synthesis. We analyze the modeling capabilities of such generators when trained in an adversarial fashion, and observe the new generators to achieve similar generation quality to state-of-the-art convolutional generators. We also investigate several interesting properties unique to the new architecture.},
  journal = {arXiv (Cornell University)},
  year = {2020},
  month = {01},
  doi = {10.48550/arxiv.2011.13775},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/2011.13775},
  publisher = {Cornell University}
}
@inproceedings{ivan_skorokhodov_1f2ffc2b,
  author = {Ivan Skorokhodov AND Savva Ignatyev AND Mohamed Elhoseiny},
  title = {Adversarial Generation of Continuous Images},
  abstract = {In most existing learning systems, images are typically viewed as 2D pixel arrays. However, in another paradigm gaining popularity, a 2D image is represented as an implicit neural representation (INR) — an MLP that predicts an RGB pixel value given its (x, y) coordinate. In this paper, we propose two novel architectural techniques for building INR-based image decoders: factorized multiplicative modulation and multi-scale INRs, and use them to build a state-of-the-art continuous image GAN. Previous attempts to adapt INRs for image generation were limited to MNIST-like datasets and do not scale to complex real- world data. Our proposed INR-GAN architecture improves the performance of continuous image generators by several times, greatly reducing the gap between continuous image GANs and pixel-based ones. Apart from that, we explore several exciting properties of the INR-based decoders, like out-of-the-box superresolution, meaningful image-space interpolation, accelerated inference of low-resolution images, an ability to extrapolate outside of image boundaries, and strong geometric prior. The project page is located at https://universome.github.io/inr-gan.},
  journal = {2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
  month = {06},
  doi = {10.1109/cvpr46437.2021.01061},
  issn = {},
  isbn = {},
  url = {https://doi.org/10.1109/cvpr46437.2021.01061}
}
@inproceedings{jeong_joon_park_b4db9cba,
  author = {Jeong Joon Park AND Peter Florence AND Julian Straub AND Richard Newcombe AND Steven Lovegrove},
  title = {DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation},
  abstract = {Computer graphics, 3D computer vision and robotics communities have produced multiple approaches to representing 3D geometry for rendering and reconstruction. These provide trade-offs across fidelity, efficiency and compression capabilities. In this work, we introduce DeepSDF, a learned continuous Signed Distance Function (SDF) representation of a class of shapes that enables high quality shape representation, interpolation and completion from partial and noisy 3D input data. DeepSDF, like its classical counterpart, represents a shape's surface by a continuous volumetric field: the magnitude of a point in the field represents the distance to the surface boundary and the sign indicates whether the region is inside (-) or outside (+) of the shape, hence our representation implicitly encodes a shape's boundary as the zero-level-set of the learned function while explicitly representing the classification of space as being part of the shapes interior or not. While classical SDF's both in analytical or discretized voxel form typically represent the surface of a single shape, DeepSDF can represent an entire class of shapes. Furthermore, we show state-of-the-art performance for learned 3D shape representation and completion while reducing the model size by an order of magnitude compared with previous work.},
  pages = {165-174},
  year = {2019},
  month = {06},
  doi = {10.1109/CVPR.2019.00025},
  issn = {},
  isbn = {978-1-7281-3293-8},
  url = {https://ieeexplore.ieee.org/document/8954065/},
  publisher = {IEEE},
  address = {Long Beach, CA, USA},
  file = {Park et al. - 2019 - DeepSDF Learning Continuous Signed Distance Funct.pdf:/home/localaak22/snap/zotero-snap/common/Zotero/storage/PVFM6KZD/Park et al. - 2019 - DeepSDF Learning Continuous Signed Distance Funct.pdf:application/pdf},
  urldate = {2023-09-06},
  language = {en},
  shorttitle = {DeepSDF},
  booktitle = {2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}
}
@article{baorui_ma_c6b4399b,
  author = {Baorui Ma AND Zhizhong Han AND Yu-Shen Liu AND Matthias Zwicker},
  title = {Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfaces},
  abstract = {Reconstructing continuous surfaces from 3D point clouds is a fundamental operation in 3D geometry processing. Several recent state-of-the-art methods address this problem using neural networks to learn signed distance functions (SDFs). In this paper, we introduce \textit{Neural-Pull}, a new approach that is simple and leads to high quality SDFs. Specifically, we train a neural network to pull query 3D locations to their closest points on the surface using the predicted signed distance values and the gradient at the query locations, both of which are computed by the network itself. The pulling operation moves each query location with a stride given by the distance predicted by the network. Based on the sign of the distance, this may move the query location along or against the direction of the gradient of the SDF. This is a differentiable operation that allows us to update the signed distance value and the gradient simultaneously during training. Our outperforming results under widely used benchmarks demonstrate that we can learn SDFs more accurately and flexibly for surface reconstruction and single image reconstruction than the state-of-the-art methods.},
  journal = {arXiv (Cornell University)},
  year = {2020},
  month = {01},
  doi = {10.48550/arxiv.2011.13495},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/2011.13495},
  publisher = {Cornell University}
}
@article{gene_chou_1b76fe1f,
  author = {Gene Chou AND Ilya Chugunov AND Felix Heide},
  title = {GenSDF: Two-Stage Learning of Generalizable Signed Distance Functions},
  abstract = {We investigate the generalization capabilities of neural signed distance functions (SDFs) for learning 3D object representations for unseen and unlabeled point clouds. Existing methods can fit SDFs to a handful of object classes and boast fine detail or fast inference speeds, but do not generalize well to unseen shapes. We introduce a two-stage semi-supervised meta-learning approach that transfers shape priors from labeled to unlabeled data to reconstruct unseen object categories. The first stage uses an episodic training scheme to simulate training on unlabeled data and meta-learns initial shape priors. The second stage then introduces unlabeled data with disjoint classes in a semi-supervised scheme to diversify these priors and achieve generalization. We assess our method on both synthetic data and real collected point clouds. Experimental results and analysis validate that our approach outperforms existing neural SDF methods and is capable of robust zero-shot inference on 100+ unseen classes. Code can be found at https://github.com/princeton-computational-imaging/gensdf.},
  journal = {arXiv (Cornell University)},
  year = {2022},
  month = {01},
  doi = {10.48550/arxiv.2206.02780},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/2206.02780},
  publisher = {Cornell University}
}
@article{gene_chou_15b03087,
  author = {Gene Chou AND Yuval Bahat AND Felix Heide},
  title = {Diffusion-SDF: Conditional Generative Modeling of Signed Distance Functions},
  abstract = {Probabilistic diffusion models have achieved state-of-the-art results for image synthesis, inpainting, and text-to-image tasks. However, they are still in the early stages of generating complex 3D shapes. This work proposes Diffusion-SDF, a generative model for shape completion, single-view reconstruction, and reconstruction of real-scanned point clouds. We use neural signed distance functions (SDFs) as our 3D representation to parameterize the geometry of various signals (e.g., point clouds, 2D images) through neural networks. Neural SDFs are implicit functions and diffusing them amounts to learning the reversal of their neural network weights, which we solve using a custom modulation module. Extensive experiments show that our method is capable of both realistic unconditional generation and conditional generation from partial inputs. This work expands the domain of diffusion models from learning 2D, explicit representations, to 3D, implicit representations.},
  journal = {arXiv (Cornell University)},
  year = {2022},
  month = {01},
  doi = {10.48550/arXiv.2211.13757},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/2211.13757},
  publisher = {Cornell University}
}
@book{gimin_nam_3021cfcc,
  author = {Gimin Nam AND Mariem Khlifi AND Andrew Rodriguez AND Alberto Tono AND Linqi Zhou AND Paul Guerrero},
  title = {3D-LDM: Neural Implicit 3D Shape Generation with Latent Diffusion Models},
  abstract = {Diffusion models have shown great promise for image generation, beating GANs in terms of generation diversity, with comparable image quality. However, their application to 3D shapes has been limited to point or voxel representations that can in practice not accurately represent a 3D surface. We propose a diffusion model for neural implicit representations of 3D shapes that operates in the latent space of an auto-decoder. This allows us to generate diverse and high quality 3D surfaces. We additionally show that we can condition our model on images or text to enable image-to-3D generation and text-to-3D generation using CLIP embeddings. Furthermore, adding noise to the latent codes of existing shapes allows us to explore shape variations.},
  year = {2022},
  month = {12},
  doi = {10.48550/arxiv.2212.00842},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/2212.00842},
  publisher = {Cornell University},
  file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/AE3H82ES/Nam et al. - 2022 - 3D-LDM Neural Implicit 3D Shape Generation with L.pdf:application/pdf},
  shorttitle = {3D-LDM}
}
@article{ziya_erko__912f08cc,
  author = {Ziya Erkoç AND Fangchang Ma AND Qi Shan AND Matthias Nießner AND Angela Dai},
  title = {HyperDiffusion: Generating Implicit Neural Fields with Weight-Space Diffusion},
  abstract = {Implicit neural fields, typically encoded by a multilayer perceptron (MLP) that maps from coordinates (e.g., xyz) to signals (e.g., signed distances), have shown remarkable promise as a high-fidelity and compact representation. However, the lack of a regular and explicit grid structure also makes it challenging to apply generative modeling directly on implicit neural fields in order to synthesize new data. To this end, we propose HyperDiffusion, a novel approach for unconditional generative modeling of implicit neural fields. HyperDiffusion operates directly on MLP weights and generates new neural implicit fields encoded by synthesized MLP parameters. Specifically, a collection of MLPs is first optimized to faithfully represent individual data samples. Subsequently, a diffusion process is trained in this MLP weight space to model the underlying distribution of neural implicit fields. HyperDiffusion enables diffusion modeling over a implicit, compact, and yet high-fidelity representation of complex signals across 3D shapes and 4D mesh animations within one single unified framework.},
  journal = {arXiv (Cornell University)},
  year = {2023},
  month = {01},
  doi = {10.48550/arxiv.2303.17015},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/2303.17015},
  publisher = {Cornell University}
}
@article{x__zheng_fba25414,
  author = {X. Zheng AND Yang Liu AND Peng‐Shuai Wang AND Xin Tong},
  title = {SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generation},
  abstract = {We present a StyleGAN2-based deep learning approach for 3D shape generation, called SDF-StyleGAN, with the aim of reducing visual and geometric dissimilarity between generated shapes and a shape collection. We extend StyleGAN2 to 3D generation and utilize the implicit signed distance function (SDF) as the 3D shape representation, and introduce two novel global and local shape discriminators that distinguish real and fake SDF values and gradients to significantly improve shape geometry and visual quality. We further complement the evaluation metrics of 3D generative models with the shading-image-based Fr\'echet inception distance (FID) scores to better assess visual quality and shape distribution of the generated shapes. Experiments on shape generation demonstrate the superior performance of SDF-StyleGAN over the state-of-the-art. We further demonstrate the efficacy of SDF-StyleGAN in various tasks based on GAN inversion, including shape reconstruction, shape completion from partial point clouds, single-view image-based shape generation, and shape style editing. Extensive ablation studies justify the efficacy of our framework design. Our code and trained models are available at https://github.com/Zhengxinyang/SDF-StyleGAN.},
  journal = {arXiv (Cornell University)},
  year = {2022},
  month = {01},
  doi = {10.48550/arxiv.2206.12055},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/2206.12055},
  publisher = {Cornell University}
}
@misc{dogyun_park_499cc0be,
  author = {Dogyun Park AND Sihyeon Kim AND Sojin Lee AND Hyunwoo J. Kim},
  title = {DDMI: Domain-Agnostic Latent Diffusion Models for Synthesizing High-Quality Implicit Neural Representations},
  abstract = {Recent studies have introduced a new class of generative models for synthesizing implicit neural representations (INRs) that capture arbitrary continuous signals in various domains. These models opened the door for domain-agnostic generative models, but they often fail to achieve high-quality generation. We observed that the existing methods generate the weights of neural networks to parameterize INRs and evaluate the network with fixed positional embeddings (PEs). Arguably, this architecture limits the expressive power of generative models and results in low-quality INR generation. To address this limitation, we propose Domain-agnostic Latent Diffusion Model for INRs (DDMI) that generates adaptive positional embeddings instead of neural networks' weights. Specifically, we develop a Discrete-to-continuous space Variational AutoEncoder (D2C-VAE), which seamlessly connects discrete data and the continuous signal functions in the shared latent space. Additionally, we introduce a novel conditioning mechanism for evaluating INRs with the hierarchically decomposed PEs to further enhance expressive power. Extensive experiments across four modalities, e.g., 2D images, 3D shapes, Neural Radiance Fields, and videos, with seven benchmark datasets, demonstrate the versatility of DDMI and its superior performance compared to the existing INR generative models.},
  year = {2024},
  month = {03},
  doi = {10.48550/arXiv.2401.12517},
  issn = {},
  isbn = {},
  url = {http://arxiv.org/abs/2401.12517},
  publisher = {arXiv},
  note = {arXiv:2401.12517},
  keywords = {Computer Science - Machine Learning},
  file = {Preprint PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/QXG4SD45/Park et al. - 2024 - DDMI Domain-Agnostic Latent Diffusion Models for Synthesizing High-Quality Implicit Neural Represen.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/P9I7YFY4/2401.html:text/html},
  shorttitle = {DDMI},
  urldate = {2024-11-15}
}
@inbook{songyou_peng_ecc7708b,
  author = {Songyou Peng AND Michael Niemeyer AND Lars Mescheder AND Marc Pollefeys AND Andreas Geiger},
  title = {Convolutional Occupancy Networks},
  journal = {Lecture notes in computer science},
  pages = {523-540},
  year = {2020},
  month = {01},
  doi = {10.1007/978-3-030-58580-8_31},
  issn = {0302-9743, 1611-3349},
  isbn = {},
  url = {https://doi.org/10.1007/978-3-030-58580-8_31},
  publisher = {Springer Science+Business Media}
}
@misc{vincent_sitzmann_4ff78465,
  author = {Vincent Sitzmann AND Julien N. P. Martel AND Alexander W. Bergman AND David B. Lindell AND Gordon Wetzstein},
  title = {Implicit Neural Representations with Periodic Activation Functions},
  abstract = {Implicitly defined, continuous, differentiable signal representations parameterized by neural networks have emerged as a powerful paradigm, offering many possible benefits over conventional representations. However, current network architectures for such implicit neural representations are incapable of modeling signals with fine detail, and fail to represent a signal's spatial and temporal derivatives, despite the fact that these are essential to many physical signals defined implicitly as the solution to partial differential equations. We propose to leverage periodic activation functions for implicit neural representations and demonstrate that these networks, dubbed sinusoidal representation networks or Sirens, are ideally suited for representing complex natural signals and their derivatives. We analyze Siren activation statistics to propose a principled initialization scheme and demonstrate the representation of images, wavefields, video, sound, and their derivatives. Further, we show how Sirens can be leveraged to solve challenging boundary value problems, such as particular Eikonal equations (yielding signed distance functions), the Poisson equation, and the Helmholtz and wave equations. Lastly, we combine Sirens with hypernetworks to learn priors over the space of Siren functions.},
  year = {2020},
  month = {06},
  doi = {10.48550/arxiv.2006.09661},
  issn = {},
  isbn = {},
  url = {http://arxiv.org/abs/2006.09661},
  publisher = {arXiv},
  file = {PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/FBJPD3S7/Sitzmann et al. - 2020 - Implicit Neural Representations with Periodic Activation Functions.pdf:application/pdf},
  keywords = {Computer Science - Machine Learning},
  language = {en},
  note = {arXiv:2006.09661 [cs]},
  urldate = {2024-11-13}
}
@inproceedings{julian_chibane_192ae504,
  author = {Julian Chibane AND Thiemo Alldieck AND Gerard Pons‐Moll},
  title = {Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion},
  abstract = {While many works focus on 3D reconstruction from images, in this paper, we focus on 3D shape reconstruction and completion from a variety of 3D inputs, which are deficient in some respect: low and high resolution voxels, sparse and dense point clouds, complete or incomplete. Processing of such 3D inputs is an increasingly important problem as they are the output of 3D scanners, which are becoming more accessible, and are the intermediate output of 3D computer vision algorithms. Recently, learned implicit functions have shown great promise as they produce continuous reconstructions. However, we identified two limitations in reconstruction from 3D inputs: 1) details present in the input data are not retained, and 2) poor reconstruction of articulated humans. To solve this, we propose Implicit Feature Networks (IF-Nets), which deliver continuous outputs, can handle multiple topologies, and complete shapes for missing or sparse input data retaining the nice properties of recent learned implicit functions, but critically they can also retain detail when it is present in the input data, and can reconstruct articulated humans. Our work differs from prior work in two crucial aspects. First, instead of using a single vector to encode a 3D shape, we extract a learnable 3-dimensional multi-scale tensor of deep features, which is aligned with the original Euclidean space embedding the shape. Second, instead of classifying x-y-z point coordinates directly, we classify deep features extracted from the tensor at a continuous query point. We show that this forces our model to make decisions based on global and local shape structure, as opposed to point coordinates, which are arbitrary under Euclidean transformations. Experiments demonstrate that IF-Nets outperform prior work in 3D object reconstruction in ShapeNet, and obtain significantly more accurate 3D human reconstructions. Code and project website is available at https://virtualhumans.mpi-inf.mpg.de/ifnets/.},
  journal = {2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages = {6968-6979},
  year = {2020},
  month = {06},
  doi = {10.1109/cvpr42600.2020.00700},
  issn = {},
  isbn = {},
  url = {https://doi.org/10.1109/cvpr42600.2020.00700}
}
@article{yifan_wang_12e075c8,
  author = {Yifan Wang AND Lukas Rahmann AND Olga Sorkine‐Hornung},
  title = {Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields},
  abstract = {We present implicit displacement fields, a novel representation for detailed 3D geometry. Inspired by a classic surface deformation technique, displacement mapping, our method represents a complex surface as a smooth base surface plus a displacement along the base's normal directions, resulting in a frequency-based shape decomposition, where the high frequency signal is constrained geometrically by the low frequency signal. Importantly, this disentanglement is unsupervised thanks to a tailored architectural design that has an innate frequency hierarchy by construction. We explore implicit displacement field surface reconstruction and detail transfer and demonstrate superior representational power, training stability and generalizability.},
  journal = {arXiv (Cornell University)},
  year = {2021},
  month = {01},
  doi = {10.48550/arxiv.2106.05187},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/2106.05187},
  publisher = {Cornell University}
}
@article{g__malinger_590c1d40,
  author = {G. Malinger AND Ana Monteagudo AND G. Pilu AND Ilan E. Timor‐Tritsch AND Ants Toi},
  title = {Sonographic examination of the fetal central nervous system: guidelines for performing the ‘basic examination’ and the ‘fetal neurosonogram’},
  journal = {Ultrasound in Obstetrics and Gynecology},
  volume = {29},
  issue = {1},
  pages = {109-116},
  year = {2007},
  month = {01},
  doi = {10.1002/uog.3909},
  pmid = {17200992},
  issn = {0960-7692, 1469-0705},
  isbn = {},
  url = {https://doi.org/10.1002/uog.3909},
  publisher = {Wiley}
}
@article{cecilia_bottomley_303222ff,
  author = {Cecilia Bottomley AND T. Bourne},
  title = {Dating and growth in the first trimester},
  abstract = {Measurement of embryonic or foetal size using the greatest length of the embryo or foetal crown rump length can be used to accurately determine the gestational age of a normal first trimester pregnancy to within three to five days. Transvaginal ultrasound scan can be used to measure the size of an embryo and gestation sac earlier than transabdominal ultrasound. The original Robinson curve used for dating pregnancies is still valid in most cases. Ultrasound dating in the first trimester is now recommended for all women with spontaneous pregnancies, even those with certain menstrual dates. First trimester growth in normal pregnancy is not uniform and is influenced by both maternal and foetal factors. Early foetal growth restriction is demonstrated in many pregnancies that subsequently end in first trimester miscarriage and is also demonstrated in fetuses with triploidy, trisomy 18 and possibly trisomy 13. Pregnancies which are small at the 11–14 week ultrasound scan appear to be at risk of later intrauterine growth restriction, preeclampsia and preterm delivery. Cross-sectional and serial measurement of foetal growth in the first trimester may be helpful in predicting both miscarriage and adverse late pregnancy outcomes.},
  journal = {Best Practice & Research Clinical Obstetrics & Gynaecology},
  volume = {23},
  issue = {4},
  pages = {439-452},
  year = {2009},
  month = {03},
  doi = {10.1016/j.bpobgyn.2009.01.011},
  pmid = {19282247},
  issn = {1521-6934, 1532-1932},
  isbn = {},
  url = {https://doi.org/10.1016/j.bpobgyn.2009.01.011}
}
@misc{fenqiang_zhao_124056ba,
  author = {Fenqiang Zhao AND Zhengwang Wu AND Gang Li},
  title = {Deep learning in cortical surface-based neuroimage analysis: a systematic review},
  abstract = {Deep learning approaches, especially convolutional neural networks (CNNs), have become the method of choice in the field of medical image analysis over the last few years. This prevalence is attributed to their excellent abilities to learn features in a more effective and efficient manner, not only for 2D/3D images in the Euclidean space, but also for meshes and graphs in non-Euclidean space such as cortical surfaces in neuroimaging analysis field. The brain cerebral cortex is a highly convoluted and thin sheet of gray matter (GM) that is thus typically represented by triangular surface meshes with an intrinsic spherical topology for each hemisphere. Accordingly, novel tailored deep learning methods have been developed for cortical surface-based analysis of neuroimaging data. This paper reviewsed the representative deep learning techniques relevant to cortical surface-based analysis and summarizes recent major contributions to the field. Specifically, we surveyed the use of deep learning techniques for cortical surface reconstruction, registration, parcellation, prediction, and other applications. We concluded by discussing the open challenges, limitations, and potentials of these techniques, and suggested directions for future research.},
  journal = {Intelligent Medicine},
  volume = {3},
  issue = {1},
  pages = {46-58},
  year = {2022},
  month = {07},
  doi = {10.1016/j.imed.2022.06.002},
  issn = {2667-1026, 2096-9376},
  isbn = {},
  url = {https://doi.org/10.1016/j.imed.2022.06.002},
  publisher = {Elsevier BV}
}
@article{benjamin_becker_d09d2a59,
  author = {Benjamin Becker AND Ignacio Sarasúa AND Christian Wachinger},
  title = {Discriminative and Generative Models for Anatomical Shape Analysison Point Clouds with Deep Neural Networks},
  abstract = {We introduce deep neural networks for the analysis of anatomical shapes that learn a low-dimensional shape representation from the given task, instead of relying on hand-engineered representations. Our framework is modular and consists of several computing blocks that perform fundamental shape processing tasks. The networks operate on unordered point clouds and provide invariance to similarity transformations, avoiding the need to identify point correspondences between shapes. Based on the framework, we assemble a discriminative model for disease classification and age regression, as well as a generative model for the accruate reconstruction of shapes. In particular, we propose a conditional generative model, where the condition vector provides a mechanism to control the generative process. instance, it enables to assess shape variations specific to a particular diagnosis, when passing it as side information. Next to working on single shapes, we introduce an extension for the joint analysis of multiple anatomical structures, where the simultaneous modeling of multiple structures can lead to a more compact encoding and a better understanding of disorders. We demonstrate the advantages of our framework in comprehensive experiments on real and synthetic data. The key insights are that (i) learning a shape representation specific to the given task yields higher performance than alternative shape descriptors, (ii) multi-structure analysis is both more efficient and more accurate than single-structure analysis, and (iii) point clouds generated by our model capture morphological differences associated to Alzheimers disease, to the point that they can be used to train a discriminative model for disease classification. Our framework naturally scales to the analysis of large datasets, giving it the potential to learn characteristic variations in large populations.},
  journal = {arXiv (Cornell University)},
  year = {2020},
  month = {01},
  doi = {10.48550/arxiv.2010.00820},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/2010.00820},
  publisher = {Cornell University}
}
@article{benjamin_becker_93c94feb,
  author = {Benjamin Becker AND Tassilo Klein AND Christian Wachinger},
  title = {Gaussian Process Uncertainty in Age Estimation as a Measure of Brain Abnormality},
  abstract = {Multivariate regression models for age estimation are a powerful tool for assessing abnormal brain morphology associated to neuropathology. Age prediction models are built on cohorts of healthy subjects and are built to reflect normal aging patterns. The application of these multivariate models to diseased subjects usually results in high prediction errors, under the hypothesis that neuropathology presents a similar degenerative pattern as that of accelerated aging. In this work, we propose an alternative to the idea that pathology follows a similar trajectory than normal aging. Instead, we propose the use of metrics which measure deviations from the mean aging trajectory. We propose to measure these deviations using two different metrics: uncertainty in a Gaussian process regression model and a newly proposed age weighted uncertainty measure. Consequently, our approach assumes that pathologic brain patterns are different to those of normal aging. We present results for subjects with autism, mild cognitive impairment and Alzheimer's disease to highlight the versatility of the approach to different diseases and age ranges. We evaluate volume, thickness, and VBM features for quantifying brain morphology. Our evaluations are performed on a large number of images obtained from a variety of publicly available neuroimaging databases. Across all features, our uncertainty based measurements yield a better separation between diseased subjects and healthy individuals than the prediction error. Finally, we illustrate differences in the disease pattern to normal aging, supporting the application of uncertainty as a measure of neuropathology.},
  journal = {arXiv (Cornell University)},
  year = {2018},
  month = {01},
  doi = {10.48550/arxiv.1804.01296},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/1804.01296},
  publisher = {Cornell University}
}
@inbook{vitalis_vosylius_b1af661a,
  author = {Vitalis Vosylius AND Andy Wang AND Cemlyn Waters AND Alexey Zakharov AND Francis Ward AND Loïc Le Folgoc AND John Cupitt AND Antonios Makropoulos AND Andreas Schuh AND Daniel Rueckert AND Amir Alansary},
  title = {Geometric Deep Learning for Post-Menstrual Age Prediction Based on the Neonatal White Matter Cortical Surface},
  journal = {Lecture notes in computer science},
  pages = {174-186},
  year = {2020},
  month = {01},
  doi = {10.1007/978-3-030-60365-6_17},
  issn = {0302-9743, 1611-3349},
  isbn = {},
  url = {https://doi.org/10.1007/978-3-030-60365-6_17},
  publisher = {Springer Science+Business Media}
}
@article{huili_sun_9b6a2ddf,
  author = {Huili Sun AND Saloni Mehta AND Milana Khaitova AND Bin Cheng AND Xuejun Hao AND Marisa N. Spann AND Dustin Scheinost},
  title = {Brain age prediction and deviations from normative trajectories in the neonatal connectome},
  abstract = {Structural and functional connectomes undergo rapid changes during the third trimester and the first month of postnatal life. Despite progress, our understanding of the developmental trajectories of the connectome in the perinatal period remains incomplete. Brain age prediction uses machine learning to estimate the brain's maturity relative to normative data. The difference between the individual's predicted and chronological age—or brain age gap (BAG)—represents the deviation from these normative trajectories. Here, we assess brain age prediction and BAGs using structural and functional connectomes for infants in the first month of life. We use resting-state fMRI and DTI data from 611 infants (174 preterm; 437 term) from the Developing Human Connectome Project (dHCP) and connectome-based predictive modeling to predict postmenstrual age (PMA). Structural and functional connectomes accurately predict PMA for term and preterm infants. Predicted ages from each modality are correlated. At the network level, nearly all canonical brain networks—even putatively later developing ones—generate accurate PMA prediction. Additionally, BAGs are associated with perinatal exposures and toddler behavioral outcomes. Overall, our results underscore the importance of normative modeling and deviations from these models during the perinatal period. Here, the authors show that altered brain development of infants after birth, driven by perinatal exposures, influences long-term cognitive outcomes.},
  journal = {Nature Communications},
  volume = {15},
  issue = {1},
  year = {2024},
  month = {11},
  doi = {10.1038/s41467-024-54657-5},
  pmid = {39592647},
  issn = {2041-1723},
  isbn = {},
  url = {https://doi.org/10.1038/s41467-024-54657-5},
  publisher = {Nature Portfolio}
}
@article{simon_dahan_fcffb5cf,
  author = {Simon Dahan AND Abdulah Fawaz AND Mohamed Suliman AND Mariana da Silva AND Logan Z. J. Williams AND Daniel Rueckert AND Emma C. Robinson},
  title = {The Multiscale Surface Vision Transformer},
  abstract = {Surface meshes are a favoured domain for representing structural and functional information on the human cortex, but their complex topology and geometry pose significant challenges for deep learning analysis. While Transformers have excelled as domain-agnostic architectures for sequence-to-sequence learning, notably for structures where the translation of the convolution operation is non-trivial, the quadratic cost of the self-attention operation remains an obstacle for many dense prediction tasks. Inspired by some of the latest advances in hierarchical modelling with vision transformers, we introduce the Multiscale Surface Vision Transformer (MS-SiT) as a backbone architecture for surface deep learning. The self-attention mechanism is applied within local-mesh-windows to allow for high-resolution sampling of the underlying data, while a shifted-window strategy improves the sharing of information between windows. Neighbouring patches are successively merged, allowing the MS-SiT to learn hierarchical representations suitable for any prediction task. Results demonstrate that the MS-SiT outperforms existing surface deep learning methods for neonatal phenotyping prediction tasks using the Developing Human Connectome Project (dHCP) dataset. Furthermore, building the MS-SiT backbone into a U-shaped architecture for surface segmentation demonstrates competitive results on cortical parcellation using the UK Biobank (UKB) and manually-annotated MindBoggle datasets. Code and trained models are publicly available at https://github.com/metrics-lab/surface-vision-transformers .},
  journal = {arXiv (Cornell University)},
  year = {2023},
  month = {01},
  doi = {10.48550/arxiv.2303.11909},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/2303.11909},
  publisher = {Cornell University}
}
@article{simon_dahan_b0160aa7,
  author = {Simon Dahan AND Hao Xu AND Logan Z. J. Williams AND Abdulah Fawaz AND Chunhui Yang AND Timothy S. Coalson AND Michelle C. Williams AND David E. Newby AND Anne Edwards AND Matthew F. Glasser AND Alistair A. Young AND Daniel Rueckert AND Emma C. Robinson},
  title = {Surface Vision Transformers: Flexible Attention-Based Modelling of Biomedical Surfaces},
  abstract = {Recent state-of-the-art performances of Vision Transformers (ViT) in computer vision tasks demonstrate that a general-purpose architecture, which implements long-range self-attention, could replace the local feature learning operations of convolutional neural networks. In this paper, we extend ViTs to surfaces by reformulating the task of surface learning as a sequence-to-sequence learning problem, by proposing patching mechanisms for general surface meshes. Sequences of patches are then processed by a transformer encoder and used for classification or regression. We validate our method on a range of different biomedical surface domains and tasks: brain age prediction in the developing Human Connectome Project (dHCP), fluid intelligence prediction in the Human Connectome Project (HCP), and coronary artery calcium score classification using surfaces from the Scottish Computed Tomography of the Heart (SCOT-HEART) dataset, and investigate the impact of pretraining and data augmentation on model performance. Results suggest that Surface Vision Transformers (SiT) demonstrate consistent improvement over geometric deep learning methods for brain age and fluid intelligence prediction and achieve comparable performance on calcium score classification to standard metrics used in clinical practice. Furthermore, analysis of transformer attention maps offers clear and individualised predictions of the features driving each task. Code is available on Github: https://github.com/metrics-lab/surface-vision-transformers},
  journal = {arXiv (Cornell University)},
  year = {2022},
  month = {01},
  doi = {10.48550/arxiv.2204.03408},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/2204.03408},
  publisher = {Cornell University}
}
@article{julian_suk_7afd1133,
  author = {Julian Suk AND Baris Imre AND Jelmer M. Wolterink},
  title = {LaB-GATr: geometric algebra transformers for large biomedical surface
  and volume meshes},
  abstract = {Many anatomical structures can be described by surface or volume meshes. Machine learning is a promising tool to extract information from these 3D models. However, high-fidelity meshes often contain hundreds of thousands of vertices, which creates unique challenges in building deep neural network architectures. Furthermore, patient-specific meshes may not be canonically aligned which limits the generalisation of machine learning algorithms. We propose LaB-GATr, a transfomer neural network with geometric tokenisation that can effectively learn with large-scale (bio-)medical surface and volume meshes through sequence compression and interpolation. Our method extends the recently proposed geometric algebra transformer (GATr) and thus respects all Euclidean symmetries, i.e. rotation, translation and reflection, effectively mitigating the problem of canonical alignment between patients. LaB-GATr achieves state-of-the-art results on three tasks in cardiovascular hemodynamics modelling and neurodevelopmental phenotype prediction, featuring meshes of up to 200,000 vertices. Our results demonstrate that LaB-GATr is a powerful architecture for learning with high-fidelity meshes which has the potential to enable interesting downstream applications. Our implementation is publicly available.},
  journal = {arXiv (Cornell University)},
  year = {2024},
  month = {03},
  doi = {10.48550/arxiv.2403.07536},
  issn = {},
  isbn = {},
  url = {http://arxiv.org/abs/2403.07536},
  publisher = {Cornell University}
}
@article{bruce_fischl_06c7226a,
  author = {Bruce Fischl},
  title = {FreeSurfer},
  journal = {NeuroImage},
  volume = {62},
  issue = {2},
  pages = {774-781},
  year = {2012},
  month = {01},
  doi = {10.1016/j.neuroimage.2012.01.021},
  pmid = {22248573},
  issn = {1053-8119, 1095-9572},
  isbn = {},
  url = {https://doi.org/10.1016/j.neuroimage.2012.01.021},
  publisher = {Elsevier BV}
}
@article{tom_vercauteren_413413d8,
  author = {Tom Vercauteren AND Xavier Pennec AND Aymeric Perchant AND Nicholas Ayache},
  title = {Diffeomorphic demons: Efficient non-parametric image registration},
  journal = {NeuroImage},
  volume = {45},
  issue = {1},
  year = {2008},
  month = {11},
  doi = {10.1016/j.neuroimage.2008.10.040},
  pmid = {19041946},
  issn = {1053-8119, 1095-9572},
  isbn = {},
  url = {https://doi.org/10.1016/j.neuroimage.2008.10.040},
  publisher = {Elsevier BV}
}
@article{yong_wang_21cf7944,
  author = {Yong Wang},
  title = {Gauss–Newton method},
  abstract = {Abstract The Gauss–Newton method for nonlinear regression is described, along with its close relationship with the Fisher scoring method. Its many extensions, sometimes in the form of Fisher scoring, are briefly reviewed, including those for coping with large‐residual problems in nonlinear regression, for optimizing a likelihood or other performance criterion, and for dealing with parameters with constraints. WIREs Comput Stat 2012 doi: 10.1002/wics.1202 This article is categorized under: Algorithms and Computational Methods &gt; Quadratic and Nonlinear Programming Algorithms and Computational Methods &gt; Numerical Methods},
  journal = {Wiley Interdisciplinary Reviews Computational Statistics},
  volume = {4},
  issue = {4},
  pages = {415-420},
  year = {2012},
  month = {02},
  doi = {10.1002/wics.1202},
  issn = {1939-0068, 1939-5108},
  isbn = {},
  url = {https://doi.org/10.1002/wics.1202},
  publisher = {Wiley}
}
@article{jieyu_cheng_e6e54e72,
  author = {Jieyu Cheng AND Adrian V. Dalca AND Bruce Fischl AND Lilla Zollei},
  title = {Cortical surface registration using unsupervised learning},
  abstract = {Non-rigid cortical registration is an important and challenging task due to the geometric complexity of the human cortex and the high degree of inter-subject variability. A conventional solution is to use a spherical representation of surface properties and perform registration by aligning cortical folding patterns in that space. This strategy produces accurate spatial alignment but often requires a high computational cost. Recently, convolutional neural networks (CNNs) have demonstrated the potential to dramatically speed up volumetric registration. However, due to distortions introduced by projecting a sphere to a 2D plane, a direct application of recent learning-based methods to surfaces yields poor results. In this study, we present SphereMorph, a diffeomorphic registration framework for cortical surfaces using deep networks that addresses these issues. SphereMorph uses a UNet-style network associated with a spherical kernel to learn the displacement field and warps the sphere using a modified spatial transformer layer. We propose a resampling weight in computing the data fitting loss to account for distortions introduced by polar projection, and demonstrate the performance of our proposed method on two tasks, including cortical parcellation and group-wise functional area alignment. The experiments show that the proposed SphereMorph is capable of modeling the geometric registration problem in a CNN framework and demonstrate superior registration accuracy and computational efficiency. The source code of SphereMorph will be released to the public upon acceptance of this manuscript at https://github.com/voxelmorph/spheremorph.},
  journal = {arXiv},
  year = {2020},
  month = {01},
  doi = {10.48550/ARXIV.2004.04617},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/2004.04617},
  publisher = {arXiv}
}
@article{fenqiang_zhao_791eaad3,
  author = {Fenqiang Zhao AND Zhengwang Wu AND Fan Wang AND Weili Lin AND Shunren Xia AND Dinggang Shen AND Li Wang AND Gang Li},
  title = {S3Reg: Superfast Spherical Surface Registration Based on Deep Learning},
  abstract = {Cortical surface registration is an essential step and prerequisite for surface-based neuroimaging analysis. It aligns cortical surfaces across individuals and time points to establish cross-sectional and longitudinal cortical correspondences to facilitate neuroimaging studies. Though achieving good performance, available methods are either time consuming or not flexible to extend to multiple or high dimensional features. Considering the explosive availability of large-scale and multimodal brain MRI data, fast surface registration methods that can flexibly handle multimodal features are desired. In this study, we develop a Superfast Spherical Surface Registration (S3Reg) framework for the cerebral cortex. Leveraging an end-to-end unsupervised learning strategy, S3Reg offers great flexibility in the choice of input feature sets and output similarity measures for registration, and meanwhile reduces the registration time significantly. Specifically, we exploit the powerful learning capability of spherical Convolutional Neural Network (CNN) to directly learn the deformation fields in spherical space and implement diffeomorphic design with "scaling and squaring" layers to guarantee topology-preserving deformations. To handle the polar-distortion issue, we construct a novel spherical CNN model using three orthogonal Spherical U-Nets. Experiments are performed on two different datasets to align both adult and infant multimodal cortical features. Results demonstrate that our S3Reg shows superior or comparable performance with state-of-the-art methods, while improving the registration time from 1 min to 10 sec.},
  journal = {IEEE Transactions on Medical Imaging},
  volume = {40},
  issue = {8},
  pages = {1964-1976},
  year = {2021},
  month = {03},
  doi = {10.1109/tmi.2021.3069645},
  pmid = {33784617},
  issn = {0278-0062, 1558-254X},
  isbn = {},
  url = {https://doi.org/10.1109/tmi.2021.3069645},
  publisher = {Institute of Electrical and Electronics Engineers}
}
@misc{mohamed_asuliman_f455e633,
  author = {Mohamed Asuliman},
  title = {DDR},
  year = {2022},
  month = {06},
  issn = {},
  isbn = {},
  url = {https://github.com/mohamedasuliman/DDR}
}
@article{jianxun_ren_e66c369e,
  author = {Jianxun Ren AND Ning An AND Youjia Zhang AND Danyang Wang AND Zhenyu Sun AND Lin Cong AND Weigang Cui AND Weiwei Wang AND Ying Zhou AND Wei Zhang AND Qingyu Hu AND Ping Zhang AND Dan Hu AND Danhong Wang AND Hesheng Liu},
  title = {SUGAR: Spherical ultrafast graph attention framework for cortical surface registration},
  abstract = {Cortical surface registration plays a crucial role in aligning cortical functional and anatomical features across individuals. However, conventional registration algorithms are computationally inefficient. Recently, learning-based registration algorithms have emerged as a promising solution, significantly improving processing efficiency. Nonetheless, there remains a gap in the development of a learning-based method that exceeds the state-of-the-art conventional methods simultaneously in computational efficiency, registration accuracy, and distortion control, despite the theoretically greater representational capabilities of deep learning approaches. To address the challenge, we present SUGAR, a unified unsupervised deep-learning framework for both rigid and non-rigid registration. SUGAR incorporates a U-Net-based spherical graph attention network and leverages the Euler angle representation for deformation. In addition to the similarity loss, we introduce fold and multiple distortion losses to preserve topology and minimize various types of distortions. Furthermore, we propose a data augmentation strategy specifically tailored for spherical surface registration to enhance the registration performance. Through extensive evaluation involving over 10,000 scans from 7 diverse datasets, we showed that our framework exhibits comparable or superior registration performance in accuracy, distortion, and test-retest reliability compared to conventional and learning-based methods. Additionally, SUGAR achieves remarkable sub-second processing times, offering a notable speed-up of approximately 12,000 times in registering 9,000 subjects from the UK Biobank dataset in just 32 min. This combination of high registration performance and accelerated processing time may greatly benefit large-scale neuroimaging studies.},
  journal = {Medical Image Analysis},
  volume = {94},
  pages = {103122-103122},
  year = {2024},
  month = {02},
  doi = {10.1016/j.media.2024.103122},
  pmid = {38428270},
  issn = {1361-8415, 1361-8423, 1361-8431},
  isbn = {},
  url = {https://doi.org/10.1016/j.media.2024.103122},
  publisher = {Elsevier BV}
}

@online{qiang_ma_8e8e8f3a,
  author = {Qiang Ma AND Kaili Liang AND Liu Lia AND Saga Masui AND Yourong Guo AND Chiara Nosarti AND Emma C. Robinson AND Bernhard Kainz AND Daniel Rueckert},
  title = {Medical Image Analysis},
  year = {2024},
  month = {01},
  issn = {},
  isbn = {}
}
@article{simon_dahan_4f79c86a,
  author = {Simon Dahan AND Abdulah Fawaz AND Mohamed Suliman AND Mariana da Silva AND Logan Z. J. Williams AND Daniel Rueckert AND Emma C. Robinson},
  title = {The Multiscale Surface Vision Transformer},
  abstract = {Surface meshes are a favoured domain for representing structural and functional information on the human cortex, but their complex topology and geometry pose significant challenges for deep learning analysis. While Transformers have excelled as domain-agnostic architectures for sequence-to-sequence learning, notably for structures where the translation of the convolution operation is non-trivial, the quadratic cost of the self-attention operation remains an obstacle for many dense prediction tasks. Inspired by some of the latest advances in hierarchical modelling with vision transformers, we introduce the Multiscale Surface Vision Transformer (MS-SiT) as a backbone architecture for surface deep learning. The self-attention mechanism is applied within local-mesh-windows to allow for high-resolution sampling of the underlying data, while a shifted-window strategy improves the sharing of information between windows. Neighbouring patches are successively merged, allowing the MS-SiT to learn hierarchical representations suitable for any prediction task. Results demonstrate that the MS-SiT outperforms existing surface deep learning methods for neonatal phenotyping prediction tasks using the Developing Human Connectome Project (dHCP) dataset. Furthermore, building the MS-SiT backbone into a U-shaped architecture for surface segmentation demonstrates competitive results on cortical parcellation using the UK Biobank (UKB) and manually-annotated MindBoggle datasets. Code and trained models are publicly available at https://github.com/metrics-lab/surface-vision-transformers .},
  journal = {arXiv (Cornell University)},
  year = {2023},
  month = {01},
  doi = {10.48550/arXiv.2303.11909},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/2303.11909},
  publisher = {Cornell University}
}
@article{ilya_loshchilov_facd4b86,
  author = {Ilya Loshchilov AND Frank Hutter},
  title = {Decoupled Weight Decay Regularization},
  abstract = {L$_2$ regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is \emph{not} the case for adaptive gradient algorithms, such as Adam. While common implementations of these algorithms employ L$_2$ regularization (often calling it "weight decay" in what may be misleading due to the inequivalence we expose), we propose a simple modification to recover the original formulation of weight decay regularization by \emph{decoupling} the weight decay from the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our proposed modification (i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam and (ii) substantially improves Adam's generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter). Our proposed decoupled weight decay has already been adopted by many researchers, and the community has implemented it in TensorFlow and PyTorch; the complete source code for our experiments is available at https://github.com/loshchil/AdamW-and-SGDW},
  journal = {arXiv (Cornell University)},
  year = {2017},
  month = {01},
  doi = {10.48550/arxiv.1711.05101},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/1711.05101},
  publisher = {Cornell University}
}
@inproceedings{gene_chou_56833c52,
  author = {Gene Chou AND Yuval Bahat AND Felix Heide},
  title = {Diffusion-SDF: Conditional Generative Modeling of Signed Distance Functions},
  abstract = {Probabilistic diffusion models have achieved state-of-the-art results for image synthesis, inpainting, and text-to-image tasks. However, they are still in the early stages of generating complex 3D shapes. This work proposes Diffusion-SDF, a generative model for shape completion, single-view reconstruction, and reconstruction of real-scanned point clouds. We use neural signed distance functions (SDFs) as our 3D representation to parameterize the geometry of various signals (e.g., point clouds, 2D images) through neural networks. Neural SDFs are implicit functions and diffusing them amounts to learning the reversal of their neural network weights, which we solve using a custom modulation module. Extensive experiments show that our method is capable of both realistic unconditional generation and conditional generation from partial inputs. This work expands the domain of diffusion models from learning 2D, explicit representations, to 3D, implicit representations. Code is released at https://github.com/princeton-computational-imaging/Diffusion-SDF.},
  journal = {2021 IEEE/CVF International Conference on Computer Vision (ICCV)},
  pages = {2262-2272},
  year = {2023},
  month = {10},
  doi = {10.1109/iccv51070.2023.00215},
  issn = {},
  isbn = {},
  url = {https://doi.org/10.1109/iccv51070.2023.00215}
}
@inbook{florentin_bieder_2df8745b,
  author = {Florentin Bieder AND Paul Friedrich AND Hélène Corbaz AND Alicia Durrer AND Julia Wolleb AND Philippe C. Cattin},
  title = {Modeling the Neonatal Brain Development Using Implicit Neural Representations},
  journal = {Lecture notes in computer science},
  pages = {1-11},
  year = {2024},
  month = {10},
  doi = {10.1007/978-3-031-74561-4_1},
  issn = {0302-9743, 1611-3349},
  isbn = {},
  url = {https://doi.org/10.1007/978-3-031-74561-4_1},
  publisher = {Springer Science+Business Media}
}


@article{li_atypical_2021,
	title = {Atypical sulcal pattern in boys with attention‐deficit/hyperactivity disorder},
	volume = {42},
	issn = {1065-9471},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8356996/},
	doi = {10.1002/hbm.25552},
	abstract = {Neurodevelopmental disorders, such as attention‐deficit/hyperactivity disorder (ADHD), are often accompanied by disrupted cortical folding. We applied a quantitative sulcal pattern analysis technique using graph structures to study the atypical cortical folding at the lobar level in ADHD brains in this study. A total of 183 ADHD patients and 167 typical developmental controls matched according to age and gender were enrolled. We first constructed sulcal graphs at the brain lobar level and then investigated their similarity to the typical sulcal patterns. The within‐group variability and interhemispheric similarity in sulcal patterns were also compared between the ADHD and TDC groups. The results showed that, compared with controls, the left frontal, right parietal, and temporal lobes displayed altered similarities to the typical sulcal patterns in patients with ADHD. Moreover, the sulcal patterns in ADHD seem to be more heterogeneous than those in controls. The results also identified the disruption of the typical asymmetric sulcal patterns in the frontal lobe between the ADHD and control groups. Taken together, our results revealed the atypical sulcal pattern in boys with ADHD and provide new insights into the neuroanatomical mechanisms of ADHD., We applied a quantitative sulcal pattern analysis technique using graph structures was applied to study the atypical cortical folding at the lobar level in attention‐deficit/hyperactivity disorder (ADHD) brains. And the results revealed the atypical sulcal pattern in boys with ADHD and provide new insights into the neuroanatomical mechanisms of ADHD.},
	number = {13},
	urldate = {2025-06-01},
	journal = {Hum Brain Mapp},
	author = {Li, Xinwei and Wang, Wei and Wang, Panyu and Hao, Chenru and Li, Zhangyong},
	month = may,
	year = {2021},
	pmid = {34057775},
	pmcid = {PMC8356996},
	pages = {4362--4371},
}

@INPROCEEDINGS{10635738,
  author={Auriau, Pierre and Grigis, Antoine and Dufumier, Benoit and Louiset, Robin and Chavas, Joël and Gori, Pietro and Mangin, Jean-François and Duchesnay, Edouard},
  booktitle={2024 IEEE International Symposium on Biomedical Imaging (ISBI)}, 
  title={Supervised Diagnosis Prediction from Cortical Sulci: Toward the Discovery of Neurodevelopmental Biomarkers in Mental Disorders}, 
  year={2024},
  volume={},
  number={},
  pages={1-5},
  abstract={Recent advances in machine learning applied to structural magnetic resonance imaging (sMRI) may highlight abnormalities in brain anatomy associated with mental disorders. These disorders are multifactorial, resulting from a complex combination of neurodevelopmental and environmental factors. In particular, such factors are present in cortical sulci, whose shapes are determined very early in brain development and are a valuable proxy for capturing specifically the neurodevelopmental contribution of brain anatomy. This paper explores whether the shapes of cortical sulci can be used for diagnosis prediction using deep learning models. These models are applied to three mental disorders (autism spectrum disorder, bipolar disorder, and schizophrenia) in large multicentric datasets. We demonstrate that the neurodevelopmental underpinnings of these disorders can be captured with sMRI. Finally, we show the potential of visual explanations of models’ decisions in discovering biomarkers for mental disorders.},
  keywords={Visualization;Shape;Mental disorders;Biological system modeling;Magnetic resonance imaging;Biomarkers;Predictive models;deep learning;cortical sulci;psychiatric disorders;neurodevelopment},
  doi={10.1109/ISBI56570.2024.10635738},
  ISSN={1945-8452},
  month={May},}


@article{bruce_fischl_1a48dc36,
  author = {Bruce Fischl AND Anders M. Dale},
  title = {Measuring the thickness of the human cerebral cortex from magnetic resonance images},
  abstract = {Accurate and automated methods for measuring the thickness of human cerebral cortex could provide powerful tools for diagnosing and studying a variety of neurodegenerative and psychiatric disorders. Manual methods for estimating cortical thickness from neuroimaging data are labor intensive, requiring several days of effort by a trained anatomist. Furthermore, the highly folded nature of the cortex is problematic for manual techniques, frequently resulting in measurement errors in regions in which the cortical surface is not perpendicular to any of the cardinal axes. As a consequence, it has been impractical to obtain accurate thickness estimates for the entire cortex in individual subjects, or group statistics for patient or control populations. Here, we present an automated method for accurately measuring the thickness of the cerebral cortex across the entire brain and for generating cross-subject statistics in a coordinate system based on cortical anatomy. The intersubject standard deviation of the thickness measures is shown to be less than 0.5 mm, implying the ability to detect focal atrophy in small populations or even individual subjects. The reliability and accuracy of this new method are assessed by within-subject test–retest studies, as well as by comparison of cross-subject regional thickness measures with published values.},
  journal = {Proceedings of the National Academy of Sciences},
  volume = {97},
  issue = {20},
  pages = {11050-11055},
  year = {2000},
  month = {09},
  doi = {10.1073/pnas.200033797},
  pmid = {10984517},
  issn = {0027-8424, 1091-6490},
  isbn = {},
  url = {https://doi.org/10.1073/pnas.200033797},
  publisher = {National Academy of Sciences}
}
@article{alan_anticevic_6d1e4dad,
  author = {Alan Anticevic AND Donna Dierker AND Sarah K. Gillespie AND Grega Repovš AND John G. Csernansky AND David C. Van Essen AND Deanna M. Barch},
  title = {Comparing surface-based and volume-based analyses of functional neuroimaging data in patients with schizophrenia},
  journal = {NeuroImage},
  volume = {41},
  issue = {3},
  pages = {835-848},
  year = {2008},
  month = {03},
  doi = {10.1016/j.neuroimage.2008.02.052},
  pmid = {18434199},
  issn = {1053-8119, 1095-9572},
  isbn = {},
  url = {https://doi.org/10.1016/j.neuroimage.2008.02.052},
  publisher = {Elsevier BV}
}
@article{david_c__van_essen_60073c4b,
  author = {David C. Van Essen AND Chad J. Donahue AND Timothy S. Coalson AND Henry Kennedy AND Takuya Hayashi AND Matthew F. Glasser},
  title = {Cerebral cortical folding, parcellation, and connectivity in humans, nonhuman primates, and mice},
  abstract = {Advances in neuroimaging and neuroanatomy have yielded major insights concerning fundamental principles of cortical organization and evolution, thus speaking to how well different species serve as models for human brain function in health and disease. Here, we focus on cortical folding, parcellation, and connectivity in mice, marmosets, macaques, and humans. Cortical folding patterns vary dramatically across species, and individual variability in cortical folding increases with cortical surface area. Such issues are best analyzed using surface-based approaches that respect the topology of the cortical sheet. Many aspects of cortical organization can be revealed using 1 type of information (modality) at a time, such as maps of cortical myelin content. However, accurate delineation of the entire mosaic of cortical areas requires a multimodal approach using information about function, architecture, connectivity, and topographic organization. Comparisons across the 4 aforementioned species reveal dramatic differences in the total number and arrangement of cortical areas, particularly between rodents and primates. Hemispheric variability and bilateral asymmetry are most pronounced in humans, which we evaluated using a high-quality multimodal parcellation of hundreds of individuals. Asymmetries include modest differences in areal size but not in areal identity. Analyses of cortical connectivity using anatomical tracers reveal highly distributed connectivity and a wide range of connection weights in monkeys and mice; indirect measures using functional MRI suggest a similar pattern in humans. Altogether, a multifaceted but integrated approach to exploring cortical organization in primate and nonprimate species provides complementary advantages and perspectives.},
  journal = {Proceedings of the National Academy of Sciences},
  volume = {116},
  issue = {52},
  pages = {26173-26180},
  year = {2019},
  month = {12},
  doi = {10.1073/pnas.1902299116},
  pmid = {31871175},
  issn = {0027-8424, 1091-6490},
  isbn = {},
  url = {https://doi.org/10.1073/pnas.1902299116},
  publisher = {National Academy of Sciences}
}

@article{nikolaas_n__oosterhof_2f677ae9,
  author = {Nikolaas N. Oosterhof AND Tobias Wiestler AND Paul E. Downing AND Jörn Diedrichsen},
  title = {A comparison of volume-based and surface-based multi-voxel pattern analysis},
  journal = {NeuroImage},
  volume = {56},
  issue = {2},
  pages = {593-600},
  year = {2010},
  month = {06},
  doi = {10.1016/j.neuroimage.2010.04.270},
  pmid = {20621701},
  issn = {1053-8119, 1095-9572},
  isbn = {},
  url = {https://doi.org/10.1016/j.neuroimage.2010.04.270},
  publisher = {Elsevier BV}
}


@article{ASHBURNER200795,
title = {A fast diffeomorphic image registration algorithm},
journal = {NeuroImage},
volume = {38},
number = {1},
pages = {95-113},
year = {2007},
issn = {1053-8119},
doi = {https://doi.org/10.1016/j.neuroimage.2007.07.007},
url = {https://www.sciencedirect.com/science/article/pii/S1053811907005848},
author = {John Ashburner},
abstract = {This paper describes DARTEL, which is an algorithm for diffeomorphic image registration. It is implemented for both 2D and 3D image registration and has been formulated to include an option for estimating inverse consistent deformations. Nonlinear registration is considered as a local optimisation problem, which is solved using a Levenberg–Marquardt strategy. The necessary matrix solutions are obtained in reasonable time using a multigrid method. A constant Eulerian velocity framework is used, which allows a rapid scaling and squaring method to be used in the computations. DARTEL has been applied to intersubject registration of 471 whole brain images, and the resulting deformations were evaluated in terms of how well they encode the shape information necessary to separate male and female subjects and to predict the ages of the subjects.}
}

@article{louise_guillon_e6ea2da9,
  author = {Louise Guillon AND Bastien Cagna AND Benoît Dufumier AND Joël Chavas AND Denis Rivière AND Jean‐François Mangin},
  title = {Detection of Abnormal Folding Patterns with Unsupervised Deep Generative Models},
  journal = {Lecture notes in computer science},
  pages = {63-72},
  year = {2021},
  month = {01},
  doi = {10.1007/978-3-030-87586-2_7},
  issn = {0302-9743, 1611-3349},
  isbn = {},
  url = {https://doi.org/10.1007/978-3-030-87586-2_7},
  publisher = {Springer Science+Business Media}
}
@article{pierre_auriau_a9fdca94,
  author = {Pierre Auriau AND Antoine Grigis AND Benoît Dufumier AND Robin Louiset AND Joël Chavas AND Pietro Gori AND Jean‐François Mangin AND Édouard Duchesnay},
  title = {Supervised Diagnosis Prediction from Cortical Sulci: Toward the Discovery of Neurodevelopmental Biomarkers in Mental Disorders},
  pages = {1-5},
  year = {2024},
  month = {05},
  doi = {10.1109/isbi56570.2024.10635738},
  issn = {},
  isbn = {},
  url = {https://doi.org/10.1109/isbi56570.2024.10635738}
}
@misc{aymeric_gaudin_44cd9196,
  author = {Aymeric Gaudin AND Louise Guillon AND Clara Fischer AND Arnaud Cachia AND Denis Rivière AND Jean-François Mangin AND Joël Chavas},
  title = {Optimizing contrastive learning for cortical folding pattern detection},
  abstract = {The human cerebral cortex has many bumps and grooves called gyri and sulci. Even though there is a high interindividual consistency for the main cortical folds, this is not the case when we examine the exact shapes and details of the folding patterns. Because of this complexity, characterizing the cortical folding variability and relating them to subjects' behavioral characteristics or pathologies is still an open scientific problem. Classical approaches include labeling a few specific patterns, either manually or semi-automatically, based on geometric distances, but the recent availability of MRI image datasets of tens of thousands of subjects makes modern deep-learning techniques particularly attractive. Here, we build a self-supervised deep-learning model to detect folding patterns in the cingulate region. We train a contrastive self-supervised model (SimCLR) on both Human Connectome Project (1101 subjects) and UKBioBank (21070 subjects) datasets with topological-based augmentations on the cortical skeletons, which are topological objects that capture the shape of the folds. We explore several backbone architectures (convolutional network, DenseNet, and PointNet) for the SimCLR. For evaluation and testing, we perform a linear classification task on a database manually labeled for the presence of the "double-parallel" folding pattern in the cingulate region, which is related to schizophrenia characteristics. The best model, giving a test AUC of 0.76, is a convolutional network with 6 layers, a 10-dimensional latent space, a linear projection head, and using the branch-clipping augmentation. This is the first time that a self-supervised deep learning model has been applied to cortical skeletons on such a large dataset and quantitatively evaluated. We can now envisage the next step: applying it to other brain regions to detect other biomarkers. The GitHub repository is publicly available on https://github.com/neurospin-projects/2022 jchavas cingulate inhibitory control.},
  pages = {22-22},
  year = {2024},
  month = {01},
  doi = {10.1117/12.3006183},
  issn = {},
  isbn = {},
  url = {http://arxiv.org/abs/2401.18035},
  publisher = {arXiv},
  file = {Preprint PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/6FYQKZ7B/Gaudin et al. - 2024 - Optimizing contrastive learning for cortical folding pattern detection.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/SJD4NEYL/2401.html:text/html},
  keywords = {Computer Science - Machine Learning},
  urldate = {2024-11-13},
  note = {arXiv:2401.18035}
}
@article{jo_l_chavas_0ea0a577,
  author = {Joël Chavas AND Louise Guillon AND Marco Pascucci AND Benoît Dufumier AND Denis Rivière AND Jean‐François Mangin},
  title = {Unsupervised Representation Learning of Cingulate Cortical Folding Patterns},
  journal = {Lecture notes in computer science},
  pages = {77-87},
  year = {2022},
  month = {01},
  doi = {10.1007/978-3-031-16431-6_8},
  issn = {0302-9743, 1611-3349},
  isbn = {},
  url = {https://doi.org/10.1007/978-3-031-16431-6_8},
  publisher = {Springer Science+Business Media}
}
@article{hyeokjin_kwon_eede060b,
  author = {Hyeokjin Kwon AND Seungyeon Son AND Sarah U. Morton AND David Wypij AND John D. Cleveland AND Caitlin K. Rollins AND Hao Huang AND Elizabeth Goldmuntz AND Ashok Panigrahy AND Thomas Gilovich AND Wendy K. Chung AND Evdokia Anagnostou AND Ami Norris‐Brilliant AND Bruce D. Gelb AND Patrick S. McQuillen AND George A. Porter AND Martin Tristani‐Firouzi AND Mark W. Russell AND Amy E. Roberts AND Jane W. Newburger AND P. Ellen Grant AND Jongmin Lee AND Kiho Im},
  title = {Graph-based Prototype Inverse-Projection for Identifying Cortical Sulcal Pattern Abnormalities in Congenital Heart Disease},
  journal = {Medical Image Analysis},
  volume = {102},
  pages = {103538-103538},
  year = {2025},
  month = {02},
  doi = {10.1016/j.media.2025.103538},
  pmid = {40121807},
  issn = {1361-8415, 1361-8423, 1361-8431},
  isbn = {},
  url = {https://doi.org/10.1016/j.media.2025.103538},
  publisher = {Elsevier BV}
}
@article{benjamin_asschenfeldt_638b2727,
  author = {Benjamin Asschenfeldt AND Lars Evald AND Hyuk Jin Yun AND Johan Heiberg AND Leif Østergaard AND P. Ellen Grant AND Vibeke E. Hjortdal AND Kiho Im AND Simon F. Eskildsen},
  title = {Abnormal Left‐Hemispheric Sulcal Patterns in Adults With Simple Congenital Heart Defects Repaired in Childhood},
  abstract = {Background Children operated on for a simple congenital heart defect (CHD) are at risk of neurodevelopmental abnormalities. Abnormal cortical development and folding have been observed in fetuses with CHD. We examined whether sulcal folding patterns in adults operated on for simple CHD in childhood differ from those of healthy controls, and whether such differences are associated with neuropsychological outcomes. Methods and Results Patients (mean age, 24.5 years) who underwent childhood surgery for isolated atrial septal defect (ASD; n=33) or ventricular septal defect (VSD; n=30) and healthy controls (n=37) were enrolled. Sulcal pattern similarity to healthy controls was determined using magnetic resonance imaging and looking at features of sulcal folds, their intersulcal relationships, and sulcal graph topology. The sulcal pattern similarity values were tested for associations with comprehensive neuropsychological scores. Patients with both ASD and VSD had decreased sulcal pattern similarity in the left hemisphere compared with controls. The differences were found in the left temporal lobe in the ASD group and in the whole left hemisphere in the VSD group (P=0.033 and P=0.039, respectively). The extent of abnormal left hemispheric sulcal pattern similarity was associated with worse neuropsychological scores (intelligence, executive function, and visuospatial abilities) in the VSD group, and special educational support in the ASD group. Conclusions Adults who underwent surgery for simple CHD in childhood display altered left hemisphere sulcal folding patterns, commensurate with neuropsychological scores for patients with VSD and special educational support for ASD. This may indicate that simple CHD affects early brain development. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT03871881.},
  journal = {Journal of the American Heart Association},
  volume = {10},
  issue = {7},
  year = {2021},
  month = {03},
  doi = {10.1161/jaha.120.018580},
  pmid = {33745293},
  issn = {2047-9980},
  isbn = {},
  url = {https://doi.org/10.1161/jaha.120.018580},
  publisher = {Wiley}
}

@unknown{unknown,
author = {Ma, Qiang and Li, Liu and Kyriakopoulou, Vanessa and Hajnal, Joseph and Robinson, Emma and Kainz, Bernhard and Rueckert, Daniel},
year = {2023},
month = {07},
pages = {},
title = {Conditional Temporal Attention Networks for Neonatal Cortical Surface Reconstruction},
doi = {10.48550/arXiv.2307.11870}
}

@article{abdulah_fawaz_c9a50635,
  author = {Abdulah Fawaz AND Saga Masui AND Logan Z. J. Williams AND Simon Dahan AND A. David Edwards AND Emma C. Robinson},
  title = {Surface Generative Modelling of Neurodevelopmental Trajectories},
  abstract = {Abstract Cortical neurodevelopment is sensitive to disruption following preterm birth, with lasting impact on cognitive outcomes. The creation of generative models of neurodevelopment could aid clinicians in identifying atrisk subjects but is complicated by the degree of subject variability in cortical folding, and significant heterogeneity in the effect of preterm birth. In this work, we propose a graph convolutional generative adversarial network (GAN) and a training scheme to simulate neonatal cortical surface developmental trajectories. The proposed model is used to smoothly modify two cortical phenotypes: post-menstrual age at scan (PMA) and gestational age at birth (GA) on data from the developing Human Connectome Project (dHCP) [1]. The synthetic images were validated with an independently trained regression network, and compared against follow up scans, indicating that the model can realistically age individuals whilst preserving subject-specific cortical morphology. Deviation between simulated ‘healthy’ scans, and preterm follow up scans generated a metric of individual atypicality, which improved prediction of 18-month cognitive outcome over GA alone.},
  journal = {bioRxiv (Cold Spring Harbor Laboratory)},
  year = {2023},
  month = {10},
  doi = {10.1101/2023.10.16.562598},
  issn = {},
  isbn = {},
  url = {https://doi.org/10.1101/2023.10.16.562598},
  publisher = {Cold Spring Harbor Laboratory}
}
@article{karthik_gopinath_5cedcc2e,
  author = {Karthik Gopinath AND Christian Desrosiers AND Hervé Lombaert},
  title = {Adaptive Graph Convolution Pooling for Brain Surface Analysis},
  journal = {Lecture notes in computer science},
  pages = {86-98},
  year = {2019},
  month = {01},
  doi = {10.1007/978-3-030-20351-1\_7},
  issn = {0302-9743, 1611-3349},
  isbn = {},
  url = {https://doi.org/10.1007/978-3-030-20351-1\_7},
  publisher = {Springer Science+Business Media}
}
@article{miaomiao_yang_dface76a,
  author = {Miaomiao Yang AND Changwei Yao AND Shijin Yan},
  title = {Age Estimation Based on Graph Convolutional Networks and Multi-head Attention Mechanisms},
  abstract = {Age estimation technology is a part of facial recognition and has been applied to identity authentication. This technology achieves the development and application of a juvenile anti-addiction system by authenticating users in the game. Convolutional Neural Network (CNN) and Transformer algorithms are widely used in this application scenario. However, these two models cannot flexibly extract and model features of faces with irregular shapes, and they are ineffective in capturing key information. Furthermore, the above methods will contain a lot of background information while extracting features, which will interfere with the model. In consequence, it is easy to extract redundant information from images. In this paper, a new modeling idea is proposed to solve this problem, which can flexibly model irregular objects. The Graph Convolutional Network (GCN) is used to extract features from irregular face images effectively, and multi-head attention mechanisms are added to avoid redundant features and capture key region information in the image. This model can effectively improve the accuracy of age estimation and reduce the MAE error value to about 3.64, which is better than the effect of today's age estimation model, to improve the accuracy of face recognition and identity authentication.},
  journal = {arXiv (Cornell University)},
  year = {2023},
  month = {01},
  doi = {10.48550/arxiv.2310.08064},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/2310.08064},
  publisher = {Cornell University}
}
@inbook{vitalis_vosylius_b1af661a,
  author = {Vitalis Vosylius AND Andy Wang AND Cemlyn Waters AND Alexey Zakharov AND Francis Ward AND Loïc Le Folgoc AND John Cupitt AND Antonios Makropoulos AND Andreas Schuh AND Daniel Rueckert AND Amir Alansary},
  title = {Geometric Deep Learning for Post-Menstrual Age Prediction Based on the Neonatal White Matter Cortical Surface},
  journal = {Lecture notes in computer science},
  pages = {174-186},
  year = {2020},
  month = {01},
  doi = {10.1007/978-3-030-60365-6\_17},
  issn = {0302-9743, 1611-3349},
  isbn = {},
  url = {https://doi.org/10.1007/978-3-030-60365-6\_17},
  publisher = {Springer Science+Business Media}
}
@article{mengyun_qiao_157a9df4,
  author = {Mengyun Qiao AND Kathryn A. McGurk AND Shuo Wang AND Paul M. Matthews AND Declan P. O’Regan AND Wenjia Bai},
  title = {A personalized time-resolved 3D mesh generative model for unveiling normal heart dynamics},
  abstract = {Abstract Understanding the structure and motion of the heart is crucial for diagnosing and managing cardiovascular diseases, the leading cause of global death. There is wide variation in cardiac shape and motion patterns, influenced by demographic, anthropometric and disease factors. Unravelling normal patterns of shape and motion, and understanding how each individual deviates from the norm, would facilitate accurate diagnosis and personalized treatment strategies. Here, to this end, we developed a conditional generative model, MeshHeart, to learn the distribution of shape and motion patterns for the left and right ventricles of the heart. To model the high-dimensional spatio-temporal mesh data, MeshHeart uses a geometric encoder to represent cardiac meshes in a latent space and a temporal transformer to model the motion dynamics of latent representations. Based on MeshHeart, we investigate the latent space of 3D + t cardiac mesh sequences and propose a distance metric, latent delta, which quantifies the deviation of a real heart from its personalized normative pattern. Here, 3D + t refers to three-dimensional data evolving over time. In experiments using a large cardiac magnetic resonance image dataset of 38,309 participants from the UK Biobank, MeshHeart demonstrates high performance in cardiac mesh sequence reconstruction and generation. Latent space features are discriminative for cardiac disease classification, whereas latent delta exhibits strong correlations with clinical phenotypes in phenome-wide association studies.},
  journal = {Nature Machine Intelligence},
  volume = {7},
  issue = {5},
  pages = {800-811},
  year = {2025},
  month = {05},
  doi = {10.1038/s42256-025-01035-5},
  pmid = {40416329},
  issn = {2522-5839},
  isbn = {},
  url = {https://doi.org/10.1038/s42256-025-01035-5},
  publisher = {Nature Portfolio}
}
@article{fabian_bongratz_ae43fa3f,
  author = {Fabian Bongratz AND Jan Fecht AND Anne-Marie Rickmann AND Christian Wachinger},
  title = {V2C-Long: Longitudinal Cortex Reconstruction with Spatiotemporal Correspondence},
  abstract = {Abstract Reconstructing the cortex from longitudinal magnetic resonance imaging (MRI) is indispensable for analyzing morphological alterations in the human brain. Despite the recent advancement of cortical surface reconstruction with deep learning, challenges arising from longitudinal data are still persistent. Especially the lack of strong spatiotemporal point correspondence between highly convoluted brain surfaces hinders downstream analyses, as local morphology is not directly comparable if the anatomical location is not matched precisely. To address this issue, we present V2C-Long, the first dedicated deep learning-based cortex reconstruction method for longitudinal MRI. V2C-Long exhibits strong inherent spatiotemporal correspondence across subjects and visits, thereby reducing the need for surface-based post-processing. We establish this correspondence directly during the reconstruction via the composition of two deep template-deformation networks and innovative aggregation of within-subject templates in mesh space. We validate V2C-Long on two large neuroimaging studies, focusing on surface accuracy, consistency, generalization, test-retest reliability, and sensitivity. The results reveal a substantial improvement in longitudinal consistency and accuracy compared to existing methods. In addition, we demonstrate stronger evidence for longitudinal cortical atrophy in Alzheimer’s disease than longitudinal FreeSurfer.},
  journal = {Imaging Neuroscience},
  year = {2025},
  month = {01},
  doi = {10.1162/imag\_a\_00500},
  issn = {2837-6056},
  isbn = {},
  url = {https://doi.org/10.1162/imag\_a\_00500},
  publisher = {The MIT Press}
}
@article{anne_marie_rickmann_7122dc69,
  author = {Anne-Marie Rickmann AND Fabian Bongratz AND Christian Wachinger},
  title = {Vertex Correspondence in Cortical Surface Reconstruction},
  journal = {Lecture notes in computer science},
  pages = {318-327},
  year = {2023},
  month = {01},
  doi = {10.1007/978-3-031-43993-3\_31},
  issn = {0302-9743, 1611-3349},
  isbn = {},
  url = {https://doi.org/10.1007/978-3-031-43993-3\_31},
  publisher = {Springer Science+Business Media}
}
@article{fabian_bongratz_33628815,
  author = {Fabian Bongratz AND Anne-Marie Rickmann AND Sebastian Pölsterl AND Christian Wachinger},
  title = {Vox2Cortex: Fast Explicit Reconstruction of Cortical Surfaces from 3D MRI Scans with Geometric Deep Neural Networks},
  abstract = {The reconstruction of cortical surfaces from brain magnetic resonance imaging (MRI) scans is essential for quantitative analyses of cortical thickness and sulcal morphology. Although traditional and deep learning-based algorithmic pipelines exist for this purpose, they have two major drawbacks: lengthy runtimes of multiple hours (traditional) or intricate post-processing, such as mesh extraction and topology correction (deep learning-based). In this work, we address both of these issues and propose Vox2Cortex, a deep learning-based algorithm that directly yields topologically correct, three-dimensional meshes of the boundaries of the cortex. Vox2Cortex leverages convolutional and graph convolutional neural networks to deform an initial template to the densely folded geometry of the cortex represented by an input MRI scan. We show in extensive experiments on three brain MRI datasets that our meshes are as accurate as the ones reconstructed by state-of-the-art methods in the field, without the need for time- and resource-intensive post-processing. To accurately reconstruct the tightly folded cortex, we work with meshes containing about 168,000 vertices at test time, scaling deep explicit reconstruction methods to a new level.},
  journal = {arXiv (Cornell University)},
  year = {2022},
  month = {01},
  doi = {10.48550/arxiv.2203.09446},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/2203.09446},
  publisher = {Cornell University}
}
@article{rana_hanocka_082c23d4,
  author = {Rana Hanocka AND Amir Hertz AND Noa Fish AND Raja Giryes AND Shachar Fleishman AND Daniel Cohen‐Or},
  title = {MeshCNN},
  abstract = {Polygonal meshes provide an efficient representation for 3D shapes. They explicitly capture both shape surface and topology, and leverage non-uniformity to represent large flat regions as well as sharp, intricate features. This non-uniformity and irregularity, however, inhibits mesh analysis efforts using neural networks that combine convolution and pooling operations. In this paper, we utilize the unique properties of the mesh for a direct analysis of 3D shapes using MeshCNN, a convolutional neural network designed specifically for triangular meshes. Analogous to classic CNNs, MeshCNN combines specialized convolution and pooling layers that operate on the mesh edges, by leveraging their intrinsic geodesic connections. Convolutions are applied on edges and the four edges of their incident triangles, and pooling is applied via an edge collapse operation that retains surface topology, thereby, generating new mesh connectivity for the subsequent convolutions. MeshCNN learns which edges to collapse, thus forming a task-driven process where the network exposes and expands the important features while discarding the redundant ones. We demonstrate the effectiveness of our task-driven pooling on various learning tasks applied to 3D meshes.},
  journal = {ACM Transactions on Graphics},
  volume = {38},
  issue = {4},
  pages = {1-12},
  year = {2019},
  month = {07},
  doi = {10.1145/3306346.3322959},
  issn = {0730-0301, 1557-7368},
  isbn = {},
  url = {https://doi.org/10.1145/3306346.3322959},
  publisher = {Association for Computing Machinery}
}
@misc{prashanth_chandran_5f194572,
  author = {Prashanth Chandran AND Gaspard Zoss AND Markus Groß AND Paulo Gotardo AND Derek Bradley},
  title = {Shape Transformers: Topology‐Independent 3D Shape Models Using Transformers},
  abstract = {Abstract Parametric 3D shape models are heavily utilized in computer graphics and vision applications to provide priors on the observed variability of an object's geometry ( e.g ., for faces). Original models were linear and operated on the entire shape at once. They were later enhanced to provide localized control on different shape parts separately. In deep shape models, nonlinearity was introduced via a sequence of fully‐connected layers and activation functions, and locality was introduced in recent models that use mesh convolution networks. As common limitations, these models often dictate, in one way or another, the allowed extent of spatial correlations and also require that a fixed mesh topology be specified ahead of time. To overcome these limitations, we present Shape Transformers , a new nonlinear parametric 3D shape model based on transformer architectures. A key benefit of this new model comes from using the transformer's self‐attention mechanism to automatically learn nonlinear spatial correlations for a class of 3D shapes. This is in contrast to global models that correlate everything and local models that dictate the correlation extent. Our transformer 3D shape autoencoder is a better alternative to mesh convolution models, which require specially‐crafted convolution, and down/up‐sampling operators that can be difficult to design. Our model is also topologically independent: it can be trained once and then evaluated on any mesh topology, unlike most previous methods. We demonstrate the application of our model to different datasets, including 3D faces, 3D hand shapes and full human bodies. Our experiments demonstrate the strong potential of our Shape Transformer model in several applications in computer graphics and vision.},
  pages = {195-207},
  year = {2022},
  month = {05},
  doi = {10.1111/cgf.14468},
  issn = {},
  isbn = {},
  url = {https://onlinelibrary.wiley.com/doi/epdf/10.1111/cgf.14468},
  publisher = {Wiley},
  file = {Shape Transformers\textbackslash{}: Topology‐Independent 3D Shape Models Using Transformers:/home/localaak22/snap/zotero-snap/common/Zotero/storage/3IEGXUH2/cgf.html:text/html},
  urldate = {2024-11-01}
}
@inproceedings{yusuke_yoshiyasu_be8961bd,
  author = {Yusuke Yoshiyasu},
  title = {Deformable Mesh Transformer for 3D Human Mesh Recovery},
  abstract = {We present Deformable mesh transFormer (DeFormer), a novel vertex-based approach to monocular 3D human mesh recovery. DeFormer iteratively fits a body mesh model to an input image via a mesh alignment feedback loop formed within a transformer decoder that is equipped with efficient body mesh driven attention modules: 1) body sparse self-attention and 2) deformable mesh cross attention. As a result, DeFormer can effectively exploit high-resolution image feature maps and a dense mesh model which were computationally expensive to deal with in previous approaches using the standard transformer attention. Experimental results show that DeFormer achieves state-of-the-art performances on the Human3.6M and 3DPW benchmarks. Ablation study is also conducted to show the effectiveness of the DeFormer model designs for leveraging multi-scale feature maps. Code is available at https://github.com/yusukey03012/DeFormer.},
  pages = {17006-17015},
  year = {2023},
  month = {06},
  doi = {10.1109/CVPR52729.2023.01631},
  issn = {},
  isbn = {979-8-3503-0129-8},
  url = {https://ieeexplore.ieee.org/document/10204623/},
  publisher = {IEEE},
  address = {Vancouver, BC, Canada},
  copyright = {https://doi.org/10.15223/policy-029},
  language = {en},
  booktitle = {2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  file = {PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/QLC776K5/Yoshiyasu - 2023 - Deformable Mesh Transformer for 3D Human Mesh Recovery.pdf:application/pdf},
  urldate = {2025-05-13}
}

@INPROCEEDINGS{7493259,
  author={Dubois, J. and Germanaud, D. and Angleys, H. and Leroy, F. and Fischer, C. and Lebenberg, J. and Lazeyras, F. and Dehaene-Lambertz, G. and Hertz-Pannier, L. and Mangin, J.-F. and Hüppi, P. and Lefèvre, J.},
  booktitle={2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)}, 
  title={Exploring the successive waves of cortical folding in the developing brain using MRI and spectral analysis of gyrification}, 
  year={2016},
  volume={},
  number={},
  pages={261-264},
  keywords={Pediatrics;Surface morphology;Spectral analysis;Magnetic resonance imaging;Brain modeling;Indexes;Computational modeling;Developing brain;gyrification;structural MRI;spectral analysis;primary folds},
  doi={10.1109/ISBI.2016.7493259}}


@misc{ding_two-stage_2025,
	title = {Two-{Stage} {Generative} {Model} for {Intracranial} {Aneurysm} {Meshes} with {Morphological} {Marker} {Conditioning}},
	url = {http://arxiv.org/abs/2505.10407},
	doi = {10.48550/arXiv.2505.10407},
	abstract = {A generative model for the mesh geometry of intracranial aneurysms (IA) is crucial for training networks to predict blood flow forces in real time, which is a key factor affecting disease progression. This need is necessitated by the absence of a large IA image datasets. Existing shape generation methods struggle to capture realistic IA features and ignore the relationship between IA pouches and parent vessels, limiting physiological realism and their generation cannot be controlled to have specific morphological measurements. We propose AneuG, a two-stage Variational Autoencoder (VAE)-based IA mesh generator. In the first stage, AneuG generates low-dimensional Graph Harmonic Deformation (GHD) tokens to encode and reconstruct aneurysm pouch shapes, constrained to morphing energy statistics truths. GHD enables more accurate shape encoding than alternatives. In the second stage, AneuG generates parent vessels conditioned on GHD tokens, by generating vascular centreline and propagating the cross-section. AneuG's IA shape generation can further be conditioned to have specific clinically relevant morphological measurements. This is useful for studies to understand shape variations represented by clinical measurements, and for flow simulation studies to understand effects of specific clinical shape parameters on fluid dynamics. Source code and implementation details are available at https://github.com/anonymousaneug/AneuG.},
	urldate = {2025-05-29},
	publisher = {arXiv},
	author = {Ding, Wenhao and Yap, Choon Hwai and Ji, Kangjun and Castro, Simão},
	month = may,
	year = {2025},
	note = {arXiv:2505.10407 [cs]},
	keywords = {Computer Science - Machine Learning},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/AZQHKSIZ/Ding et al. - 2025 - Two-Stage Generative Model for Intracranial Aneurysm Meshes with Morphological Marker Conditioning.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/6RR44IHF/2505.html:text/html},
}

@misc{ludke_landmark-free_2022,
	title = {Landmark-free {Statistical} {Shape} {Modeling} via {Neural} {Flow} {Deformations}},
	url = {http://arxiv.org/abs/2209.06861},
	doi = {10.48550/arXiv.2209.06861},
	abstract = {Statistical shape modeling aims at capturing shape variations of an anatomical structure that occur within a given population. Shape models are employed in many tasks, such as shape reconstruction and image segmentation, but also shape generation and classification. Existing shape priors either require dense correspondence between training examples or lack robustness and topological guarantees. We present FlowSSM, a novel shape modeling approach that learns shape variability without requiring dense correspondence between training instances. It relies on a hierarchy of continuous deformation flows, which are parametrized by a neural network. Our model outperforms state-of-the-art methods in providing an expressive and robust shape prior for distal femur and liver. We show that the emerging latent representation is discriminative by separating healthy from pathological shapes. Ultimately, we demonstrate its effectiveness on two shape reconstruction tasks from partial data. Our source code is publicly available (https://github.com/davecasp/flowssm).},
	urldate = {2025-06-20},
	publisher = {arXiv},
	author = {Lüdke, David and Amiranashvili, Tamaz and Ambellan, Felix and Ezhov, Ivan and Menze, Bjoern and Zachow, Stefan},
	month = sep,
	year = {2022},
	note = {arXiv:2209.06861 [cs]},
	keywords = {Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/XBKFAZKF/Lüdke et al. - 2022 - Landmark-free Statistical Shape Modeling via Neural Flow Deformations.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/SJ8D88IC/2209.html:text/html},
}

@misc{iyer_mesh2ssm_2023,
	title = {{Mesh2SSM}: {From} {Surface} {Meshes} to {Statistical} {Shape} {Models} of {Anatomy}},
	shorttitle = {{Mesh2SSM}},
	url = {http://arxiv.org/abs/2305.07805},
	doi = {10.48550/arXiv.2305.07805},
	abstract = {Statistical shape modeling is the computational process of discovering significant shape parameters from segmented anatomies captured by medical images (such as MRI and CT scans), which can fully describe subject-specific anatomy in the context of a population. The presence of substantial non-linear variability in human anatomy often makes the traditional shape modeling process challenging. Deep learning techniques can learn complex non-linear representations of shapes and generate statistical shape models that are more faithful to the underlying population-level variability. However, existing deep learning models still have limitations and require established/optimized shape models for training. We propose Mesh2SSM, a new approach that leverages unsupervised, permutation-invariant representation learning to estimate how to deform a template point cloud to subject-specific meshes, forming a correspondence-based shape model. Mesh2SSM can also learn a population-specific template, reducing any bias due to template selection. The proposed method operates directly on meshes and is computationally efficient, making it an attractive alternative to traditional and deep learning-based SSM approaches.},
	urldate = {2025-06-20},
	publisher = {arXiv},
	author = {Iyer, Krithika and Elhabian, Shireen},
	month = jul,
	year = {2023},
	note = {arXiv:2305.07805 [cs]},
	keywords = {Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/4K55JIL9/Iyer and Elhabian - 2023 - Mesh2SSM From Surface Meshes to Statistical Shape Models of Anatomy.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/5R43NEA4/2305.html:text/html},
}

@misc{luo_explicit_2024,
	title = {Explicit {Differentiable} {Slicing} and {Global} {Deformation} for {Cardiac} {Mesh} {Reconstruction}},
	url = {http://arxiv.org/abs/2409.02070},
	doi = {10.48550/arXiv.2409.02070},
	abstract = {Mesh reconstruction of the cardiac anatomy from medical images is useful for shape and motion measurements and biophysics simulations to facilitate the assessment of cardiac function and health. However, 3D medical images are often acquired as 2D slices that are sparsely sampled and noisy, and mesh reconstruction on such data is a challenging task. Traditional voxel-based approaches rely on pre- and post-processing that compromises image fidelity, while mesh-level deep learning approaches require mesh annotations that are difficult to get. Therefore, direct cross-domain supervision from 2D images to meshes is a key technique for advancing 3D learning in medical imaging, but it has not been well-developed. While there have been attempts to approximate the optimized meshes' slicing, few existing methods directly use 2D slices to supervise mesh reconstruction in a differentiable manner. Here, we propose a novel explicit differentiable voxelization and slicing (DVS) algorithm that allows gradient backpropagation to a mesh from its slices, facilitating refined mesh optimization directly supervised by the losses defined on 2D images. Further, we propose an innovative framework for extracting patient-specific left ventricle (LV) meshes from medical images by coupling DVS with a graph harmonic deformation (GHD) mesh morphing descriptor of cardiac shape that naturally preserves mesh quality and smoothness during optimization. Experimental results demonstrate that our method achieves state-of-the-art performance in cardiac mesh reconstruction tasks from CT and MRI, with an overall Dice score of 90\% on multi-datasets, outperforming existing approaches. The proposed method can further quantify clinically useful parameters such as ejection fraction and global myocardial strains, closely matching the ground truth and surpassing the traditional voxel-based approach in sparse images.},
	urldate = {2025-06-25},
	publisher = {arXiv},
	author = {Luo, Yihao and Sesia, Dario and Wang, Fanwen and Wu, Yinzhe and Ding, Wenhao and Huang, Jiahao and Shi, Fadong and Shah, Anoop and Kaural, Amit and Mayet, Jamil and Yang, Guang and Yap, ChoonHwai},
	month = oct,
	year = {2024},
	note = {arXiv:2409.02070 [eess]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/VDE8ECR4/Luo et al. - 2024 - Explicit Differentiable Slicing and Global Deformation for Cardiac Mesh Reconstruction.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/Z5ASFEM4/2409.html:text/html},
}

@article{fu_fast_2023,
	title = {Fast three-dimensional image generation for healthy brain aging using diffeomorphic registration},
	volume = {44},
	copyright = {© 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.},
	issn = {1097-0193},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.26165},
	doi = {10.1002/hbm.26165},
	abstract = {Predicting brain aging can help in the early detection and prognosis of neurodegenerative diseases. Longitudinal cohorts of healthy subjects scanned through magnetic resonance imaging (MRI) have been essential to understand the structural brain changes due to aging. However, these cohorts suffer from missing data due to logistic issues in the recruitment of subjects. This paper proposes a methodology for filling up missing data in longitudinal cohorts with anatomically plausible images that capture the subject-specific aging process. The proposed methodology is developed within the framework of diffeomorphic registration. First, two novel modules are introduced within Synthmorph, a fast, state-of-the-art deep learning-based diffeomorphic registration method, to simulate the aging process between the first and last available MRI scan for each subject in three-dimensional (3D). The use of image registration also makes the generated images plausible by construction. Second, we used six image similarity measurements to rearrange the generated images to the specific age range. Finally, we estimated the age of every generated image by using the assumption of linear brain decay in healthy subjects. The methodology was evaluated on 2662 T1-weighted MRI scans from 796 healthy participants from 3 different longitudinal cohorts: Alzheimer's Disease Neuroimaging Initiative, Open Access Series of Imaging Studies-3, and Group of Neuropsychological Studies of the Canary Islands (GENIC). In total, we generated 7548 images to simulate the access of a scan per subject every 6 months in these cohorts. We evaluated the quality of the synthetic images using six quantitative measurements and a qualitative assessment by an experienced neuroradiologist with state-of-the-art results. The assumption of linear brain decay was accurate in these cohorts (R2 ∈ [.924, .940]). The experimental results show that the proposed methodology can produce anatomically plausible aging predictions that can be used to enhance longitudinal datasets. Compared to deep learning-based generative methods, diffeomorphic registration is more likely to preserve the anatomy of the different structures of the brain, which makes it more appropriate for its use in clinical applications. The proposed methodology is able to efficiently simulate anatomically plausible 3D MRI scans of brain aging of healthy subjects from two images scanned at two different time points.},
	language = {en},
	number = {4},
	urldate = {2025-06-26},
	journal = {Human Brain Mapping},
	author = {Fu, Jingru and Tzortzakakis, Antonios and Barroso, José and Westman, Eric and Ferreira, Daniel and Moreno, Rodrigo and Initiative, for the Alzheimer's Disease Neuroimaging},
	year = {2023},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/hbm.26165},
	keywords = {brain aging, diffeomorphic registration, medical image generation, synthetic brain aging},
	pages = {1289--1308},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/5YYP2B9H/Fu et al. - 2023 - Fast three-dimensional image generation for healthy brain aging using diffeomorphic registration.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/H2NEHC64/hbm.html:text/html},
}

@article{fu_synthesizing_2025,
	title = {Synthesizing {Individualized} {Aging} {Brains} in {Health} and {Disease} with {Generative} {Models} and {Parallel} {Transport}},
	volume = {105},
	issn = {13618415},
	url = {http://arxiv.org/abs/2502.21049},
	doi = {10.1016/j.media.2025.103669},
	abstract = {Simulating prospective magnetic resonance imaging (MRI) scans from a given individual brain image is challenging, as it requires accounting for canonical changes in aging and/or disease progression while also considering the individual brain's current status and unique characteristics. While current deep generative models can produce high-resolution anatomically accurate templates for population-wide studies, their ability to predict future aging trajectories for individuals remains limited, particularly in capturing subject-specific neuroanatomical variations over time. In this study, we introduce Individualized Brain Synthesis (InBrainSyn), a framework for synthesizing high-resolution subject-specific longitudinal MRI scans that simulate neurodegeneration in both Alzheimer's disease (AD) and normal aging. InBrainSyn uses a parallel transport algorithm to adapt the population-level aging trajectories learned by a generative deep template network, enabling individualized aging synthesis. As InBrainSyn uses diffeomorphic transformations to simulate aging, the synthesized images are topologically consistent with the original anatomy by design. We evaluated InBrainSyn both quantitatively and qualitatively on AD and healthy control cohorts from the Open Access Series of Imaging Studies - version 3 dataset. Experimentally, InBrainSyn can also model neuroanatomical transitions between normal aging and AD. An evaluation of an external set supports its generalizability. Overall, with only a single baseline scan, InBrainSyn synthesizes realistic 3D spatiotemporal T1w MRI scans, producing personalized longitudinal aging trajectories. The code for InBrainSyn is available at: https://github.com/Fjr9516/InBrainSyn.},
	urldate = {2025-06-26},
	journal = {Medical Image Analysis},
	author = {Fu, Jingru and Zheng, Yuqi and Dey, Neel and Ferreira, Daniel and Moreno, Rodrigo},
	month = oct,
	year = {2025},
	note = {arXiv:2502.21049 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence},
	pages = {103669},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/VVM7HURD/Fu et al. - 2025 - Synthesizing Individualized Aging Brains in Health and Disease with Generative Models and Parallel T.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/NJP98AKE/2502.html:text/html},
}

@misc{kubik_toothforge_2025,
	title = {{ToothForge}: {Automatic} {Dental} {Shape} {Generation} using {Synchronized} {Spectral} {Embeddings}},
	shorttitle = {{ToothForge}},
	url = {http://arxiv.org/abs/2506.02702},
	doi = {10.48550/arXiv.2506.02702},
	abstract = {We introduce ToothForge, a spectral approach for automatically generating novel 3D teeth, effectively addressing the sparsity of dental shape datasets. By operating in the spectral domain, our method enables compact machine learning modeling, allowing the generation of high-resolution tooth meshes in milliseconds. However, generating shape spectra comes with the instability of the decomposed harmonics. To address this, we propose modeling the latent manifold on synchronized frequential embeddings. Spectra of all data samples are aligned to a common basis prior to the training procedure, effectively eliminating biases introduced by the decomposition instability. Furthermore, synchronized modeling removes the limiting factor imposed by previous methods, which require all shapes to share a common fixed connectivity. Using a private dataset of real dental crowns, we observe a greater reconstruction quality of the synthetized shapes, exceeding those of models trained on unaligned embeddings. We also explore additional applications of spectral analysis in digital dentistry, such as shape compression and interpolation. ToothForge facilitates a range of approaches at the intersection of spectral analysis and machine learning, with fewer restrictions on mesh structure. This makes it applicable for shape analysis not only in dentistry, but also in broader medical applications, where guaranteeing consistent connectivity across shapes from various clinics is unrealistic. The code is available at https://github.com/tiborkubik/toothForge.},
	urldate = {2025-06-28},
	publisher = {arXiv},
	author = {Kubík, Tibor and Guibault, François and Španěl, Michal and Lombaert, Hervé},
	month = jun,
	year = {2025},
	note = {arXiv:2506.02702 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/K4CR2Q74/Kubík et al. - 2025 - ToothForge Automatic Dental Shape Generation using Synchronized Spectral Embeddings.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/M8AXVEGA/2506.html:text/html},
}

@misc{sorensen_spatio-temporal_2024,
	title = {Spatio-temporal neural distance fields for conditional generative modeling of the heart},
	url = {http://arxiv.org/abs/2407.10663},
	doi = {10.48550/arXiv.2407.10663},
	abstract = {The rhythmic pumping motion of the heart stands as a cornerstone in life, as it circulates blood to the entire human body through a series of carefully timed contractions of the individual chambers. Changes in the size, shape and movement of the chambers can be important markers for cardiac disease and modeling this in relation to clinical demography or disease is therefore of interest. Existing methods for spatio-temporal modeling of the human heart require shape correspondence over time or suffer from large memory requirements, making it difficult to use for complex anatomies. We introduce a novel conditional generative model, where the shape and movement is modeled implicitly in the form of a spatio-temporal neural distance field and conditioned on clinical demography. The model is based on an auto-decoder architecture and aims to disentangle the individual variations from that related to the clinical demography. It is tested on the left atrium (including the left atrial appendage), where it outperforms current state-of-the-art methods for anatomical sequence completion and generates synthetic sequences that realistically mimics the shape and motion of the real left atrium. In practice, this means we can infer functional measurements from a static image, generate synthetic populations with specified demography or disease and investigate how non-imaging clinical data effect the shape and motion of cardiac anatomies.},
	urldate = {2025-06-29},
	publisher = {arXiv},
	author = {Sørensen, Kristine and Diez, Paula and Margeta, Jan and Youssef, Yasmin El and Pham, Michael and Pedersen, Jonas Jalili and Kühl, Tobias and Backer, Ole de and Kofoed, Klaus and Camara, Oscar and Paulsen, Rasmus},
	month = jul,
	year = {2024},
	note = {arXiv:2407.10663 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/2JYLHNTM/Sørensen et al. - 2024 - Spatio-temporal neural distance fields for conditional generative modeling of the heart.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/TVQWE69T/2407.html:text/html},
}

@article{ziegler_models_2012,
	title = {Models of the aging brain structure and individual decline},
	volume = {6},
	issn = {1662-5196},
	url = {https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2012.00003/full},
	doi = {10.3389/fninf.2012.00003},
	abstract = {The aging brain’s structural development constitutes a spatiotemporal process that is accessible by MR-based computational morphometry. Here we introduce basic concepts and analytical approaches to quantify age-related differences and changes in neuroanatomical images of the human brain. The presented models first address the estimation of age trajectories, then we consider inter-individual variations of structural decline, using a repeated measures design. We concentrate our overview on preprocessed neuroanatomical images of the human brain to facilitate practical applications to diverse voxel- and surface-based structural markers. Together these methods afford analysis of aging brain structure in relation to behavioral, health or cognitive parameters.},
	language = {English},
	urldate = {2025-06-30},
	journal = {Front. Neuroinform.},
	author = {Ziegler, Gabriel and Dahnke, Robert and Gaser, Christian},
	month = mar,
	year = {2012},
	note = {Publisher: Frontiers},
	keywords = {Aging, brain morphometry, individual differences, longitudinal analysis, modeling, multivariate analysis},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/J44KLJM5/Ziegler et al. - 2012 - Models of the aging brain structure and individual decline.pdf:application/pdf},
}

@article{khanal_biophysical_2016,
	title = {A biophysical model of brain deformation to simulate and analyze longitudinal {MRIs} of patients with {Alzheimer}'s disease},
	volume = {134},
	issn = {1053-8119},
	url = {https://www.sciencedirect.com/science/article/pii/S1053811916300052},
	doi = {10.1016/j.neuroimage.2016.03.061},
	abstract = {We propose a framework for developing a comprehensive biophysical model that could predict and simulate realistic longitudinal MRIs of patients with Alzheimer's disease (AD). The framework includes three major building blocks: i) atrophy generation, ii) brain deformation, and iii) realistic MRI generation. Within this framework, this paper focuses on a detailed implementation of the brain deformation block with a carefully designed biomechanics-based tissue loss model. For a given baseline brain MRI, the model yields a deformation field imposing the desired atrophy at each voxel of the brain parenchyma while allowing the CSF to expand as required to globally compensate for the locally prescribed volume loss. Our approach is inspired by biomechanical principles and involves a system of equations similar to Stokes equations in fluid mechanics but with the presence of a non-zero mass source term. We use this model to simulate longitudinal MRIs by prescribing complex patterns of atrophy. We present experiments that provide an insight into the role of different biomechanical parameters in the model. The model allows simulating images with exactly the same tissue atrophy but with different underlying deformation fields in the image. We explore the influence of different spatial distributions of atrophy on the image appearance and on the measurements of atrophy reported by various global and local atrophy estimation algorithms. We also present a pipeline that allows evaluating atrophy estimation algorithms by simulating longitudinal MRIs from large number of real subject MRIs with complex subject-specific atrophy patterns. The proposed framework could help understand the implications of different model assumptions, regularization choices, and spatial priors for the detection and measurement of brain atrophy from longitudinal brain MRIs.},
	urldate = {2025-06-30},
	journal = {NeuroImage},
	author = {Khanal, Bishesh and Lorenzi, Marco and Ayache, Nicholas and Pennec, Xavier},
	month = jul,
	year = {2016},
	keywords = {Alzheimer's disease, Biophysical model, Longitudinal modeling, Longitudinal MRIs simulation, Simulation of atrophy},
	pages = {35--52},
	file = {Accepted Version:/home/localaak22/snap/zotero-snap/common/Zotero/storage/HPI3MGJT/Khanal et al. - 2016 - A biophysical model of brain deformation to simulate and analyze longitudinal MRIs of patients with.pdf:application/pdf;ScienceDirect Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/4ZSHZ6RR/S1053811916300052.html:text/html},
}

@inproceedings{cury_spatio-temporal_2016,
	address = {Cham},
	title = {Spatio-{Temporal} {Shape} {Analysis} of {Cross}-{Sectional} {Data} for {Detection} of {Early} {Changes} in {Neurodegenerative} {Disease}},
	isbn = {978-3-319-51237-2},
	doi = {10.1007/978-3-319-51237-2_6},
	abstract = {The detection of pathological changes in neurodegenerative diseases that occur before clinical onset would be of great value for identifying suitable subjects and assessing drug efficacy in trials aimed at preventing or slowing onset. Using MRI derived volumetric information, researchers have been able to detect significant differences between patients in the presymptomatic phase of neurodegenerative diseases and healthy controls. However, volumetric studies provide only a summary representation of complex morphological changes. Shape analysis has already been successfully applied to model pathological features in neurodegeneration and represents a valuable instrument to model presymptomatic anatomical changes occurring in specific brain regions.},
	language = {en},
	booktitle = {Spectral and {Shape} {Analysis} in {Medical} {Imaging}},
	publisher = {Springer International Publishing},
	author = {Cury, Claire and Lorenzi, Marco and Cash, David and Nicholas, Jennifer M. and Routier, Alexandre and Rohrer, Jonathan and Ourselin, Sebastien and Durrleman, Stanley and Modat, Marc},
	editor = {Reuter, Martin and Wachinger, Christian and Lombaert, Hervé},
	year = {2016},
	keywords = {FTD, Parallel transport, Shape, Spatio-temporal geodesic regression, Thalamus},
	pages = {63--75},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/JLW3HCH8/Cury et al. - 2016 - Spatio-Temporal Shape Analysis of Cross-Sectional Data for Detection of Early Changes in Neurodegene.pdf:application/pdf},
}

@article{cury_spatiotemporal_2019,
	title = {Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: {Initial} application to the {GENFI} cohort},
	volume = {188},
	issn = {1053-8119},
	shorttitle = {Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases},
	url = {https://www.sciencedirect.com/science/article/pii/S105381191832144X},
	doi = {10.1016/j.neuroimage.2018.11.063},
	abstract = {Brain atrophy as measured from structural MR images, is one of the primary imaging biomarkers used to track neurodegenerative disease progression. In diseases such as frontotemporal dementia or Alzheimer's disease, atrophy can be observed in key brain structures years before any clinical symptoms are present. Atrophy is most commonly captured as volume change of key structures and the shape changes of these structures are typically not analysed despite being potentially more sensitive than summary volume statistics over the entire structure. In this paper we propose a spatiotemporal analysis pipeline based on Large Diffeomorphic Deformation Metric Mapping (LDDMM) to detect shape changes from volumetric MRI scans. We applied our framework to a cohort of individuals with genetic variants of frontotemporal dementia and healthy controls from the Genetic FTD Initiative (GENFI) study. Our method, take full advantage of the LDDMM framework, and relies on the creation of a population specific average spatiotemporal trajectory of a relevant brain structure of interest, the thalamus in our case. The residuals from each patient data to the average spatiotemporal trajectory are then clustered and studied to assess when presymptomatic mutation carriers differ from healthy control subjects. We found statistical differences in shape in the anterior region of the thalamus at least five years before the mutation carrier subjects develop any clinical symptoms. This region of the thalamus has been shown to be predominantly connected to the frontal lobe, consistent with the pattern of cortical atrophy seen in the disease.},
	urldate = {2025-06-30},
	journal = {NeuroImage},
	author = {Cury, Claire and Durrleman, Stanley and Cash, David M. and Lorenzi, Marco and Nicholas, Jennifer M. and Bocchetta, Martina and van Swieten, John C. and Borroni, Barbara and Galimberti, Daniela and Masellis, Mario and Tartaglia, Maria Carmela and Rowe, James B. and Graff, Caroline and Tagliavini, Fabrizio and Frisoni, Giovanni B. and Laforce, Robert and Finger, Elizabeth and de Mendonça, Alexandre and Sorbi, Sandro and Ourselin, Sebastien and Rohrer, Jonathan D. and Modat, Marc and Andersson, Christin and Archetti, Silvana and Arighi, Andrea and Benussi, Luisa and Black, Sandra and Cosseddu, Maura and Fallstrm, Marie and Ferreira, Carlos and Fenoglio, Chiara and Fox, Nick and Freedman, Morris and Fumagalli, Giorgio and Gazzina, Stefano and Ghidoni, Roberta and Grisoli, Marina and Jelic, Vesna and Jiskoot, Lize and Keren, Ron and Lombardi, Gemma and Maruta, Carolina and Meeter, Lieke and van Minkelen, Rick and Nacmias, Benedetta and ijerstedt, Linn and Padovani, Alessandro and Panman, Jessica and Pievani, Michela and Polito, Cristina and Premi, Enrico and Prioni, Sara and Rademakers, Rosa and Redaelli, Veronica and Rogaeva, Ekaterina and Rossi, Giacomina and Rossor, Martin and Scarpini, Elio and Tang-Wai, David and Tartaglia, Carmela and Thonberg, Hakan and Tiraboschi, Pietro and Verdelho, Ana and Warren, Jason},
	month = mar,
	year = {2019},
	keywords = {Clustering, Computational anatomy, Parallel transport, Shape analysis, Spatiotemporal geodesic regression, Thalamus},
	pages = {282--290},
	file = {Full Text:/home/localaak22/snap/zotero-snap/common/Zotero/storage/P2QDQMQK/Cury et al. - 2019 - Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases.pdf:application/pdf;ScienceDirect Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/MI39TEJV/S105381191832144X.html:text/html},
}

@article{qiao_personalized_2025,
	title = {A personalized time-resolved {3D} mesh generative model for unveiling normal heart dynamics},
	volume = {7},
	copyright = {2025 The Author(s)},
	issn = {2522-5839},
	url = {https://www.nature.com/articles/s42256-025-01035-5},
	doi = {10.1038/s42256-025-01035-5},
	abstract = {Understanding the structure and motion of the heart is crucial for diagnosing and managing cardiovascular diseases, the leading cause of global death. There is wide variation in cardiac shape and motion patterns, influenced by demographic, anthropometric and disease factors. Unravelling normal patterns of shape and motion, and understanding how each individual deviates from the norm, would facilitate accurate diagnosis and personalized treatment strategies. Here, to this end, we developed a conditional generative model, MeshHeart, to learn the distribution of shape and motion patterns for the left and right ventricles of the heart. To model the high-dimensional spatio-temporal mesh data, MeshHeart uses a geometric encoder to represent cardiac meshes in a latent space and a temporal transformer to model the motion dynamics of latent representations. Based on MeshHeart, we investigate the latent space of 3D + t cardiac mesh sequences and propose a distance metric, latent delta, which quantifies the deviation of a real heart from its personalized normative pattern. Here, 3D + t refers to three-dimensional data evolving over time. In experiments using a large cardiac magnetic resonance image dataset of 38,309 participants from the UK Biobank, MeshHeart demonstrates high performance in cardiac mesh sequence reconstruction and generation. Latent space features are discriminative for cardiac disease classification, whereas latent delta exhibits strong correlations with clinical phenotypes in phenome-wide association studies.},
	language = {en},
	number = {5},
	urldate = {2025-06-30},
	journal = {Nat Mach Intell},
	author = {Qiao, Mengyun and McGurk, Kathryn A. and Wang, Shuo and Matthews, Paul M. and O’Regan, Declan P. and Bai, Wenjia},
	month = may,
	year = {2025},
	note = {Publisher: Nature Publishing Group},
	keywords = {Cardiovascular diseases, Magnetic resonance imaging},
	pages = {800--811},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/MX3WIQZU/Qiao et al. - 2025 - A personalized time-resolved 3D mesh generative model for unveiling normal heart dynamics.pdf:application/pdf},
}

@misc{adams_point2ssm_2024,
	title = {{Point2SSM}: {Learning} {Morphological} {Variations} of {Anatomies} from {Point} {Cloud}},
	shorttitle = {{Point2SSM}},
	url = {http://arxiv.org/abs/2305.14486},
	doi = {10.48550/arXiv.2305.14486},
	abstract = {We present Point2SSM, a novel unsupervised learning approach for constructing correspondence-based statistical shape models (SSMs) directly from raw point clouds. SSM is crucial in clinical research, enabling population-level analysis of morphological variation in bones and organs. Traditional methods of SSM construction have limitations, including the requirement of noise-free surface meshes or binary volumes, reliance on assumptions or templates, and prolonged inference times due to simultaneous optimization of the entire cohort. Point2SSM overcomes these barriers by providing a data-driven solution that infers SSMs directly from raw point clouds, reducing inference burdens and increasing applicability as point clouds are more easily acquired. While deep learning on 3D point clouds has seen success in unsupervised representation learning and shape correspondence, its application to anatomical SSM construction is largely unexplored. We conduct a benchmark of state-of-the-art point cloud deep networks on the SSM task, revealing their limited robustness to clinical challenges such as noisy, sparse, or incomplete input and limited training data. Point2SSM addresses these issues through an attention-based module, providing effective correspondence mappings from learned point features. Our results demonstrate that the proposed method significantly outperforms existing networks in terms of accurate surface sampling and correspondence, better capturing population-level statistics.},
	urldate = {2025-06-30},
	publisher = {arXiv},
	author = {Adams, Jadie and Elhabian, Shireen},
	month = jan,
	year = {2024},
	note = {arXiv:2305.14486 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
	file = {Preprint PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/2CFVKH5F/Adams and Elhabian - 2024 - Point2SSM Learning Morphological Variations of Anatomies from Point Cloud.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/EVYAVRZV/2305.html:text/html},
}

@misc{alblas_geometric_2025,
	title = {Geometric deep learning for local growth prediction on abdominal aortic aneurysm surfaces},
	url = {http://arxiv.org/abs/2506.08729},
	doi = {10.48550/arXiv.2506.08729},
	abstract = {Abdominal aortic aneurysms (AAAs) are progressive focal dilatations of the abdominal aorta. AAAs may rupture, with a survival rate of only 20{\textbackslash}\%. Current clinical guidelines recommend elective surgical repair when the maximum AAA diameter exceeds 55 mm in men or 50 mm in women. Patients that do not meet these criteria are periodically monitored, with surveillance intervals based on the maximum AAA diameter. However, this diameter does not take into account the complex relation between the 3D AAA shape and its growth, making standardized intervals potentially unfit. Personalized AAA growth predictions could improve monitoring strategies. We propose to use an SE(3)-symmetric transformer model to predict AAA growth directly on the vascular model surface enriched with local, multi-physical features. In contrast to other works which have parameterized the AAA shape, this representation preserves the vascular surface's anatomical structure and geometric fidelity. We train our model using a longitudinal dataset of 113 computed tomography angiography (CTA) scans of 24 AAA patients at irregularly sampled intervals. After training, our model predicts AAA growth to the next scan moment with a median diameter error of 1.18 mm. We further demonstrate our model's utility to identify whether a patient will become eligible for elective repair within two years (acc = 0.93). Finally, we evaluate our model's generalization on an external validation set consisting of 25 CTAs from 7 AAA patients from a different hospital. Our results show that local directional AAA growth prediction from the vascular surface is feasible and may contribute to personalized surveillance strategies.},
	urldate = {2025-06-30},
	publisher = {arXiv},
	author = {Alblas, Dieuwertje and Rygiel, Patryk and Suk, Julian and Kappe, Kaj O. and Hofman, Marieke and Brune, Christoph and Yeung, Kak Khee and Wolterink, Jelmer M.},
	month = jun,
	year = {2025},
	note = {arXiv:2506.08729 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition},
	file = {Preprint PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/NMB2MUR5/Alblas et al. - 2025 - Geometric deep learning for local growth prediction on abdominal aortic aneurysm surfaces.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/RGKAXDVT/2506.html:text/html},
}

@article{li_atypical_2021,
	title = {Atypical sulcal pattern in boys with attention‐deficit/hyperactivity disorder},
	volume = {42},
	issn = {1065-9471},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8356996/},
	doi = {10.1002/hbm.25552},
	abstract = {Neurodevelopmental disorders, such as attention‐deficit/hyperactivity disorder (ADHD), are often accompanied by disrupted cortical folding. We applied a quantitative sulcal pattern analysis technique using graph structures to study the atypical cortical folding at the lobar level in ADHD brains in this study. A total of 183 ADHD patients and 167 typical developmental controls matched according to age and gender were enrolled. We first constructed sulcal graphs at the brain lobar level and then investigated their similarity to the typical sulcal patterns. The within‐group variability and interhemispheric similarity in sulcal patterns were also compared between the ADHD and TDC groups. The results showed that, compared with controls, the left frontal, right parietal, and temporal lobes displayed altered similarities to the typical sulcal patterns in patients with ADHD. Moreover, the sulcal patterns in ADHD seem to be more heterogeneous than those in controls. The results also identified the disruption of the typical asymmetric sulcal patterns in the frontal lobe between the ADHD and control groups. Taken together, our results revealed the atypical sulcal pattern in boys with ADHD and provide new insights into the neuroanatomical mechanisms of ADHD., We applied a quantitative sulcal pattern analysis technique using graph structures was applied to study the atypical cortical folding at the lobar level in attention‐deficit/hyperactivity disorder (ADHD) brains. And the results revealed the atypical sulcal pattern in boys with ADHD and provide new insights into the neuroanatomical mechanisms of ADHD.},
	number = {13},
	urldate = {2025-06-01},
	journal = {Hum Brain Mapp},
	author = {Li, Xinwei and Wang, Wei and Wang, Panyu and Hao, Chenru and Li, Zhangyong},
	month = may,
	year = {2021},
	pmid = {34057775},
	pmcid = {PMC8356996},
	pages = {4362--4371},
}
@article{xia_learning_2021,
	title = {Learning to synthesise the ageing brain without longitudinal data},
	volume = {73},
	issn = {1361-8415},
	url = {https://www.sciencedirect.com/science/article/pii/S1361841521002152},
	doi = {https://doi.org/10.1016/j.media.2021.102169},
	abstract = {How will my face look when I get older? Or, for a more challenging question: How will my brain look when I get older? To answer this question one must devise (and learn from data) a multivariate auto-regressive function which given an image and a desired target age generates an output image. While collecting data for faces may be easier, collecting longitudinal brain data is not trivial. We propose a deep learning-based method that learns to simulate subject-specific brain ageing trajectories without relying on longitudinal data. Our method synthesises images conditioned on two factors: age (a continuous variable), and status of Alzheimer’s Disease (AD, an ordinal variable). With an adversarial formulation we learn the joint distribution of brain appearance, age and AD status, and define reconstruction losses to address the challenging problem of preserving subject identity. We compare with several benchmarks using two widely used datasets. We evaluate the quality and realism of synthesised images using ground-truth longitudinal data and a pre-trained age predictor. We show that, despite the use of cross-sectional data, our model learns patterns of gray matter atrophy in the middle temporal gyrus in patients with AD. To demonstrate generalisation ability, we train on one dataset and evaluate predictions on the other. In conclusion, our model shows an ability to separate age, disease influence and anatomy using only 2D cross-sectional data that should be useful in large studies into neurodegenerative disease, that aim to combine several data sources. To facilitate such future studies by the community at large our code is made available at https://github.com/xiat0616/BrainAgeing.},
	journal = {Medical Image Analysis},
	author = {Xia, Tian and Chartsias, Agisilaos and Wang, Chengjia and Tsaftaris, Sotirios A.},
	year = {2021},
	keywords = {Brain ageing, Generative adversarial network, Magnetic resonance imaging (MRI), Neurodegenerative disease},
	pages = {102169},
}

@incollection{linguraru_longitudinally_2024,
	address = {Cham},
	title = {Longitudinally {Consistent} {Individualized} {Prediction} of {Infant} {Cortical} {Morphological} {Development}},
	volume = {15005},
	isbn = {978-3-031-72085-7 978-3-031-72086-4},
	url = {https://link.springer.com/10.1007/978-3-031-72086-4_42},
	abstract = {Neurodevelopment is exceptionally dynamic and critical during infancy, as many neurodevelopmental disorders emerge from abnormal brain development during this stage. Obtaining a full trajectory of neurodevelopment from existing incomplete longitudinal data can enrich our limited understanding of normal early brain development and help identify neurodevelopmental disorders. Although many regression models and deep learning methods have been proposed for longitudinal prediction based on incomplete datasets, they have two major drawbacks. First, regression models suffered from the strict requirements of input and output time points, which is less useful in practical scenarios. Second, although existing deep learning methods could predict cortical development at multiple ages, they predicted missing data independently with each available scan, yielding inconsistent predictions for a target time point given multiple inputs, which ignores longitudinal dependencies and introduces ambiguity in practical applications. To this end, we emphasize temporal consistency and develop a novel, flexible framework named longitudinally consistent triplet disentanglement autoencoder to predict an individualized longitudinal cortical developmental trajectory based on each available input by encouraging the similarity among trajectories with a dynamic time-warping loss. Specifically, to achieve individualized prediction, we employ a surfaced-based autoencoder, which decomposes the encoded latent features into identity-related and age-related features with an age estimation task and identity similarity loss as supervisions. These identityrelated features are further combined with age conditions in the latent space to generate longitudinal developmental trajectories with the decoder. Experiments on predicting longitudinal infant cortical property maps validate the superior longitudinal consistency and exactness of our results compared to baselines’.},
	language = {en},
	urldate = {2025-05-28},
	booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2024},
	publisher = {Springer Nature Switzerland},
	author = {Yuan, Xinrui and Cheng, Jiale and Hu, Dan and Wu, Zhengwang and Wang, Li and Lin, Weili and Li, Gang},
	editor = {Linguraru, Marius George and Dou, Qi and Feragen, Aasa and Giannarou, Stamatia and Glocker, Ben and Lekadir, Karim and Schnabel, Julia A.},
	year = {2024},
	doi = {10.1007/978-3-031-72086-4_42},
	note = {Series Title: Lecture Notes in Computer Science},
	pages = {447--457},
	file = {PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/5QK93RH7/Yuan et al. - 2024 - Longitudinally Consistent Individualized Prediction of Infant Cortical Morphological Development.pdf:application/pdf},
}

@inproceedings{nie_computational_2009,
	address = {Berlin, Heidelberg},
	title = {A {Computational} {Model} of {Cerebral} {Cortex} {Folding}},
	isbn = {978-3-642-04271-3},
	doi = {10.1007/978-3-642-04271-3_56},
	abstract = {Folding of the human cerebral cortex has intrigued many people for many years. Quantitative description of cortical folding pattern and understanding of the underlying mechanisms have emerged as an important research goal. This paper presents a computational 3D geometric model of cerebral cortex folding that is initialized by MRI data of human fetus brain and deformed under the governance of partial differential equations modeling the cortical growth. The simulations of this 3D geometric model provide computational experiment support to the following hypotheses: 1) Mechanical constraints of the brain skull regulate the cortical folding process. 2) The cortical folding pattern is dependent on the global cell growth rate in the whole cortex. 3) The cortical folding pattern is dependent on relative degrees of tethering of different cortical areas and the initial geometry.},
	language = {en},
	booktitle = {Medical {Image} {Computing} and {Computer}-{Assisted} {Intervention} – {MICCAI} 2009},
	publisher = {Springer},
	author = {Nie, Jingxin and Li, Gang and Guo, Lei and Liu, Tianming},
	editor = {Yang, Guang-Zhong and Hawkes, David and Rueckert, Daniel and Noble, Alison and Taylor, Chris},
	year = {2009},
	keywords = {Deformable Model, Fetus Brain Development, Folding Pattern, Human Cerebral Cortex, Partial Differential Equation Modeling},
	pages = {458--465},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/99MD5A2U/Nie et al. - 2009 - A Computational Model of Cerebral Cortex Folding.pdf:application/pdf},
}
@Article{ai5030074,
AUTHOR = {Farea, Amer and Yli-Harja, Olli and Emmert-Streib, Frank},
TITLE = {Understanding Physics-Informed Neural Networks: Techniques, Applications, Trends, and Challenges},
JOURNAL = {AI},
VOLUME = {5},
YEAR = {2024},
NUMBER = {3},
PAGES = {1534--1557},
URL = {https://www.mdpi.com/2673-2688/5/3/74},
ISSN = {2673-2688},
ABSTRACT = {Physics-informed neural networks (PINNs) represent a significant advancement at the intersection of machine learning and physical sciences, offering a powerful framework for solving complex problems governed by physical laws. This survey provides a comprehensive review of the current state of research on PINNs, highlighting their unique methodologies, applications, challenges, and future directions. We begin by introducing the fundamental concepts underlying neural networks and the motivation for integrating physics-based constraints. We then explore various PINN architectures and techniques for incorporating physical laws into neural network training, including approaches to solving partial differential equations (PDEs) and ordinary differential equations (ODEs). Additionally, we discuss the primary challenges faced in developing and applying PINNs, such as computational complexity, data scarcity, and the integration of complex physical laws. Finally, we identify promising future research directions. Overall, this survey seeks to provide a foundational understanding of PINNs within this rapidly evolving field.},
DOI = {10.3390/ai5030074}
}

@article{baker_mechanistic_2018,
	title = {Mechanistic models versus machine learning, a fight worth fighting for the biological community?},
	volume = {14},
	url = {https://royalsocietypublishing.org/doi/10.1098/rsbl.2017.0660},
	doi = {10.1098/rsbl.2017.0660},
	abstract = {Ninety per cent of the world's data have been generated in the last 5 years (Machine learning: the power and promise of computers that learn by example. Report no. DES4702. Issued April 2017. Royal Society). A small fraction of these data is collected with the aim of validating specific hypotheses. These studies are led by the development of mechanistic models focused on the causality of input–output relationships. However, the vast majority is aimed at supporting statistical or correlation studies that bypass the need for causality and focus exclusively on prediction. Along these lines, there has been a vast increase in the use of machine learning models, in particular in the biomedical and clinical sciences, to try and keep pace with the rate of data generation. Recent successes now beg the question of whether mechanistic models are still relevant in this area. Said otherwise, why should we try to understand the mechanisms of disease progression when we can use machine learning tools to directly predict disease outcome?},
	number = {5},
	urldate = {2025-07-01},
	journal = {Biology Letters},
	author = {Baker, Ruth E. and Peña, Jose-Maria and Jayamohan, Jayaratnam and Jérusalem, Antoine},
	month = may,
	year = {2018},
	note = {Publisher: Royal Society},
	keywords = {machine learning, mechanistic modelling, quantitative biology},
	pages = {20170660},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/J7SI3ZEW/Baker et al. - 2018 - Mechanistic models versus machine learning, a fight worth fighting for the biological community.pdf:application/pdf},
}




@article{benzinger_regional_2013,
	title = {Regional variability of imaging biomarkers in autosomal dominant {Alzheimer}'s disease},
	volume = {110},
	issn = {1091-6490},
	doi = {10.1073/pnas.1317918110},
	abstract = {Major imaging biomarkers of Alzheimer's disease include amyloid deposition [imaged with [(11)C]Pittsburgh compound B (PiB) PET], altered glucose metabolism (imaged with [(18)F]fluro-deoxyglucose PET), and structural atrophy (imaged by MRI). Recently we published the initial subset of imaging findings for specific regions in a cohort of individuals with autosomal dominant Alzheimer's disease. We now extend this work to include a larger cohort, whole-brain analyses integrating all three imaging modalities, and longitudinal data to examine regional differences in imaging biomarker dynamics. The anatomical distribution of imaging biomarkers is described in relation to estimated years from symptom onset. Autosomal dominant Alzheimer's disease mutation carrier individuals have elevated PiB levels in nearly every cortical region 15 y before the estimated age of onset. Reduced cortical glucose metabolism and cortical thinning in the medial and lateral parietal lobe appeared 10 and 5 y, respectively, before estimated age of onset. Importantly, however, a divergent pattern was observed subcortically. All subcortical gray-matter regions exhibited elevated PiB uptake, but despite this, only the hippocampus showed reduced glucose metabolism. Similarly, atrophy was not observed in the caudate and pallidum despite marked amyloid accumulation. Finally, before hypometabolism, a hypermetabolic phase was identified for some cortical regions, including the precuneus and posterior cingulate. Additional analyses of individuals in which longitudinal data were available suggested that an accelerated appearance of volumetric declines approximately coincides with the onset of the symptomatic phase of the disease.},
	language = {eng},
	number = {47},
	journal = {Proc Natl Acad Sci U S A},
	author = {Benzinger, Tammie L. S. and Blazey, Tyler and Jack, Clifford R. and Koeppe, Robert A. and Su, Yi and Xiong, Chengjie and Raichle, Marcus E. and Snyder, Abraham Z. and Ances, Beau M. and Bateman, Randall J. and Cairns, Nigel J. and Fagan, Anne M. and Goate, Alison and Marcus, Daniel S. and Aisen, Paul S. and Christensen, Jon J. and Ercole, Lindsay and Hornbeck, Russ C. and Farrar, Angela M. and Aldea, Patricia and Jasielec, Mateusz S. and Owen, Christopher J. and Xie, Xianyun and Mayeux, Richard and Brickman, Adam and McDade, Eric and Klunk, William and Mathis, Chester A. and Ringman, John and Thompson, Paul M. and Ghetti, Bernardino and Saykin, Andrew J. and Sperling, Reisa A. and Johnson, Keith A. and Salloway, Stephen and Correia, Stephen and Schofield, Peter R. and Masters, Colin L. and Rowe, Christopher and Villemagne, Victor L. and Martins, Ralph and Ourselin, Sebastien and Rossor, Martin N. and Fox, Nick C. and Cash, David M. and Weiner, Michael W. and Holtzman, David M. and Buckles, Virginia D. and Moulder, Krista and Morris, John C.},
	month = nov,
	year = {2013},
	pmid = {24194552},
	pmcid = {PMC3839740},
	keywords = {Adult, Age of Onset, aging, Alzheimer Disease, Aniline Compounds, Biomarkers, Brain, Carbon Radioisotopes, Cohort Studies, dementia, DIAN, Female, Fluorodeoxyglucose F18, Genes, Dominant, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Models, Biological, neurodegeneration, neuroimaging, Positron-Emission Tomography, Regression Analysis, Thiazoles, Time Factors},
	pages = {E4502--4509},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/QRN3XNHN/Benzinger et al. - 2013 - Regional variability of imaging biomarkers in autosomal dominant Alzheimer's disease.pdf:application/pdf},
}

@article{rohrer_presymptomatic_2015,
	title = {Presymptomatic cognitive and neuroanatomical changes in genetic frontotemporal dementia in the {Genetic} {Frontotemporal} dementia {Initiative} ({GENFI}) study: a cross-sectional analysis},
	volume = {14},
	issn = {1474-4465},
	shorttitle = {Presymptomatic cognitive and neuroanatomical changes in genetic frontotemporal dementia in the {Genetic} {Frontotemporal} dementia {Initiative} ({GENFI}) study},
	doi = {10.1016/S1474-4422(14)70324-2},
	abstract = {BACKGROUND: Frontotemporal dementia is a highly heritable neurodegenerative disorder. In about a third of patients, the disease is caused by autosomal dominant genetic mutations usually in one of three genes: progranulin (GRN), microtubule-associated protein tau (MAPT), or chromosome 9 open reading frame 72 (C9orf72). Findings from studies of other genetic dementias have shown neuroimaging and cognitive changes before symptoms onset, and we aimed to identify whether such changes could be shown in frontotemporal dementia.
METHODS: We recruited participants to this multicentre study who either were known carriers of a pathogenic mutation in GRN, MAPT, or C9orf72, or were at risk of carrying a mutation because a first-degree relative was a known symptomatic carrier. We calculated time to expected onset as the difference between age at assessment and mean age at onset within the family. Participants underwent a standardised clinical assessment and neuropsychological battery. We did MRI and generated cortical and subcortical volumes using a parcellation of the volumetric T1-weighted scan. We used linear mixed-effects models to examine whether the association of neuropsychology and imaging measures with time to expected onset of symptoms differed between mutation carriers and non-carriers.
FINDINGS: Between Jan 30, 2012, and Sept 15, 2013, we recruited participants from 11 research sites in the UK, Italy, the Netherlands, Sweden, and Canada. We analysed data from 220 participants: 118 mutation carriers (40 symptomatic and 78 asymptomatic) and 102 non-carriers. For neuropsychology measures, we noted the earliest significant differences between mutation carriers and non-carriers 5 years before expected onset, when differences were significant for all measures except for tests of immediate recall and verbal fluency. We noted the largest Z score differences between carriers and non-carriers 5 years before expected onset in tests of naming (Boston Naming Test -0·7; SE 0·3) and executive function (Trail Making Test Part B, Digit Span backwards, and Digit Symbol Task, all -0·5, SE 0·2). For imaging measures, we noted differences earliest for the insula (at 10 years before expected symptom onset, mean volume as a percentage of total intracranial volume was 0·80\% in mutation carriers and 0·84\% in non-carriers; difference -0·04, SE 0·02) followed by the temporal lobe (at 10 years before expected symptom onset, mean volume as a percentage of total intracranial volume 8·1\% in mutation carriers and 8·3\% in non-carriers; difference -0·2, SE 0·1).
INTERPRETATION: Structural imaging and cognitive changes can be identified 5-10 years before expected onset of symptoms in asymptomatic adults at risk of genetic frontotemporal dementia. These findings could help to define biomarkers that can stage presymptomatic disease and track disease progression, which will be important for future therapeutic trials.
FUNDING: Centres of Excellence in Neurodegeneration.},
	language = {eng},
	number = {3},
	journal = {Lancet Neurol},
	author = {Rohrer, Jonathan D. and Nicholas, Jennifer M. and Cash, David M. and van Swieten, John and Dopper, Elise and Jiskoot, Lize and van Minkelen, Rick and Rombouts, Serge A. and Cardoso, M. Jorge and Clegg, Shona and Espak, Miklos and Mead, Simon and Thomas, David L. and De Vita, Enrico and Masellis, Mario and Black, Sandra E. and Freedman, Morris and Keren, Ron and MacIntosh, Bradley J. and Rogaeva, Ekaterina and Tang-Wai, David and Tartaglia, Maria Carmela and Laforce, Robert and Tagliavini, Fabrizio and Tiraboschi, Pietro and Redaelli, Veronica and Prioni, Sara and Grisoli, Marina and Borroni, Barbara and Padovani, Alessandro and Galimberti, Daniela and Scarpini, Elio and Arighi, Andrea and Fumagalli, Giorgio and Rowe, James B. and Coyle-Gilchrist, Ian and Graff, Caroline and Fallström, Marie and Jelic, Vesna and Ståhlbom, Anne Kinhult and Andersson, Christin and Thonberg, Håkan and Lilius, Lena and Frisoni, Giovanni B. and Pievani, Michela and Bocchetta, Martina and Benussi, Luisa and Ghidoni, Roberta and Finger, Elizabeth and Sorbi, Sandro and Nacmias, Benedetta and Lombardi, Gemma and Polito, Cristina and Warren, Jason D. and Ourselin, Sebastien and Fox, Nick C. and Rossor, Martin N. and Binetti, Giuliano},
	month = mar,
	year = {2015},
	pmid = {25662776},
	pmcid = {PMC6742501},
	keywords = {Adult, Asymptomatic Diseases, Brain, Cognition Disorders, Cross-Sectional Studies, Female, Frontotemporal Dementia, Humans, Male, Middle Aged, Mutation, Neuropsychological Tests},
	pages = {253--262},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/UTN6QIBK/Rohrer et al. - 2015 - Presymptomatic cognitive and neuroanatomical changes in genetic frontotemporal dementia in the Genet.pdf:application/pdf},
}

@article{schott_increased_2010,
	title = {Increased brain atrophy rates in cognitively normal older adults with low cerebrospinal fluid {Aβ1}-42},
	volume = {68},
	issn = {1531-8249},
	doi = {10.1002/ana.22315},
	abstract = {OBJECTIVE: To identify cognitively normal individuals at risk of Alzheimer disease (AD) based on cerebrospinal fluid (CSF) Aβ1-42, and to determine rates of cerebral atrophy.
METHODS: Control subjects from the Alzheimer's Disease Neuroimaging Initiative with CSF and serial magnetic resonance imaging (MRI) were dichotomized on CSF Aβ1-42 (normal control [NC]-high {\textgreater} 192 pg/ml; NC-low ≤ 192 pg/ml). Baseline and 1-year MRIs were registered, and brain, hippocampal, and ventricular volumes and annualized volume changes were calculated. Baseline characteristics, CSF profiles, neuropsychology, brain volumes and atrophy rates, and APOE, PICALM, CLU, and TOMM40 genotypes were compared. Sample sizes to power presymptomatic clinical trials based on rate of atrophy were calculated.
RESULTS: Forty of 105 (38\%) were classified as NC-low, and 65 (62\%) as NC-high. There were no differences in age (76.3 ± 5.1 vs 74.9 ± 5.1 years), gender, brain volumes, and all but 1 cognitive score (Trails B; p = 0.015). The NC-low group had higher tau (p = 0.005) and p-tau (p {\textless} 0.001), and was more likely to be APOE4 positive (48\% vs 11\%, p {\textless} 0.001). The NC-low group had significantly higher whole brain loss (9.3 vs 4.4 ml/yr, p {\textless} 0.001), ventricular expansion (2.04 vs 0.95 ml/yr, p = 0.002), and hippocampal atrophy rate (0.07 vs 0.03 ml/yr, p = 0.029). Baseline Aβ1-42 level was strongly correlated with rate of brain atrophy only in the NC-low group (p {\textless} 0.001). Using 141 (95\% confidence interval, 86-287) patients per arm provides 80\% power in a 1-year treatment trial to show 25\% slowing of brain atrophy in the NC-low group.
INTERPRETATION: A significant percentage of healthy older adults have CSF profiles consistent with AD and increased rates of brain atrophy, suggesting that they may be in the earliest stages of neurodegeneration. Brain atrophy may be a feasible outcome measure for AD prevention studies.},
	language = {eng},
	number = {6},
	journal = {Ann Neurol},
	author = {Schott, Jonathan M. and Bartlett, Jonathan W. and Fox, Nick C. and Barnes, Josephine and {Alzheimer's Disease Neuroimaging Initiative Investigators}},
	month = dec,
	year = {2010},
	pmid = {21181717},
	keywords = {Aged, Aged, 80 and over, Aging, Amyloid beta-Peptides, Apolipoprotein E4, Atrophy, Brain, Cognition Disorders, Disease Progression, Female, Humans, Magnetic Resonance Imaging, Male, Neuropsychological Tests, Peptide Fragments, tau Proteins},
	pages = {825--834},
}

@article{cury_spatiotemporal_2019,
	title = {Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: {Initial} application to the {GENFI} cohort},
	volume = {188},
	issn = {1053-8119},
	shorttitle = {Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases},
	url = {https://www.sciencedirect.com/science/article/pii/S105381191832144X},
	doi = {10.1016/j.neuroimage.2018.11.063},
	abstract = {Brain atrophy as measured from structural MR images, is one of the primary imaging biomarkers used to track neurodegenerative disease progression. In diseases such as frontotemporal dementia or Alzheimer's disease, atrophy can be observed in key brain structures years before any clinical symptoms are present. Atrophy is most commonly captured as volume change of key structures and the shape changes of these structures are typically not analysed despite being potentially more sensitive than summary volume statistics over the entire structure. In this paper we propose a spatiotemporal analysis pipeline based on Large Diffeomorphic Deformation Metric Mapping (LDDMM) to detect shape changes from volumetric MRI scans. We applied our framework to a cohort of individuals with genetic variants of frontotemporal dementia and healthy controls from the Genetic FTD Initiative (GENFI) study. Our method, take full advantage of the LDDMM framework, and relies on the creation of a population specific average spatiotemporal trajectory of a relevant brain structure of interest, the thalamus in our case. The residuals from each patient data to the average spatiotemporal trajectory are then clustered and studied to assess when presymptomatic mutation carriers differ from healthy control subjects. We found statistical differences in shape in the anterior region of the thalamus at least five years before the mutation carrier subjects develop any clinical symptoms. This region of the thalamus has been shown to be predominantly connected to the frontal lobe, consistent with the pattern of cortical atrophy seen in the disease.},
	urldate = {2025-07-04},
	journal = {NeuroImage},
	author = {Cury, Claire and Durrleman, Stanley and Cash, David M. and Lorenzi, Marco and Nicholas, Jennifer M. and Bocchetta, Martina and van Swieten, John C. and Borroni, Barbara and Galimberti, Daniela and Masellis, Mario and Tartaglia, Maria Carmela and Rowe, James B. and Graff, Caroline and Tagliavini, Fabrizio and Frisoni, Giovanni B. and Laforce, Robert and Finger, Elizabeth and de Mendonça, Alexandre and Sorbi, Sandro and Ourselin, Sebastien and Rohrer, Jonathan D. and Modat, Marc and Andersson, Christin and Archetti, Silvana and Arighi, Andrea and Benussi, Luisa and Black, Sandra and Cosseddu, Maura and Fallstrm, Marie and Ferreira, Carlos and Fenoglio, Chiara and Fox, Nick and Freedman, Morris and Fumagalli, Giorgio and Gazzina, Stefano and Ghidoni, Roberta and Grisoli, Marina and Jelic, Vesna and Jiskoot, Lize and Keren, Ron and Lombardi, Gemma and Maruta, Carolina and Meeter, Lieke and van Minkelen, Rick and Nacmias, Benedetta and ijerstedt, Linn and Padovani, Alessandro and Panman, Jessica and Pievani, Michela and Polito, Cristina and Premi, Enrico and Prioni, Sara and Rademakers, Rosa and Redaelli, Veronica and Rogaeva, Ekaterina and Rossi, Giacomina and Rossor, Martin and Scarpini, Elio and Tang-Wai, David and Tartaglia, Carmela and Thonberg, Hakan and Tiraboschi, Pietro and Verdelho, Ana and Warren, Jason},
	month = mar,
	year = {2019},
	keywords = {Clustering, Computational anatomy, Parallel transport, Shape analysis, Spatiotemporal geodesic regression, Thalamus},
	pages = {282--290},
	file = {Full Text:/home/localaak22/snap/zotero-snap/common/Zotero/storage/DZNRAQMQ/Cury et al. - 2019 - Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases.pdf:application/pdf;ScienceDirect Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/UQTJXM3V/S105381191832144X.html:text/html},
}

@inproceedings{cury_spatio-temporal_2016,
	address = {Cham},
	title = {Spatio-{Temporal} {Shape} {Analysis} of {Cross}-{Sectional} {Data} for {Detection} of {Early} {Changes} in {Neurodegenerative} {Disease}},
	isbn = {978-3-319-51237-2},
	doi = {10.1007/978-3-319-51237-2_6},
	abstract = {The detection of pathological changes in neurodegenerative diseases that occur before clinical onset would be of great value for identifying suitable subjects and assessing drug efficacy in trials aimed at preventing or slowing onset. Using MRI derived volumetric information, researchers have been able to detect significant differences between patients in the presymptomatic phase of neurodegenerative diseases and healthy controls. However, volumetric studies provide only a summary representation of complex morphological changes. Shape analysis has already been successfully applied to model pathological features in neurodegeneration and represents a valuable instrument to model presymptomatic anatomical changes occurring in specific brain regions.},
	language = {en},
	booktitle = {Spectral and {Shape} {Analysis} in {Medical} {Imaging}},
	publisher = {Springer International Publishing},
	author = {Cury, Claire and Lorenzi, Marco and Cash, David and Nicholas, Jennifer M. and Routier, Alexandre and Rohrer, Jonathan and Ourselin, Sebastien and Durrleman, Stanley and Modat, Marc},
	editor = {Reuter, Martin and Wachinger, Christian and Lombaert, Hervé},
	year = {2016},
	keywords = {FTD, Parallel transport, Shape, Spatio-temporal geodesic regression, Thalamus},
	pages = {63--75},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/9DTI8HE9/Cury et al. - 2016 - Spatio-Temporal Shape Analysis of Cross-Sectional Data for Detection of Early Changes in Neurodegene.pdf:application/pdf},
}

@article{delisi_concept_2008,
	title = {The {Concept} of {Progressive} {Brain} {Change} in {Schizophrenia}: {Implications} for {Understanding} {Schizophrenia}},
	volume = {34},
	issn = {0586-7614},
	shorttitle = {The {Concept} of {Progressive} {Brain} {Change} in {Schizophrenia}},
	url = {https://dx.doi.org/10.1093/schbul/sbm164},
	doi = {10.1093/schbul/sbm164},
	abstract = {Abstract. Kraepelin originally defined dementia praecox as a progressive brain disease, although this concept has received various degrees of acceptance an},
	language = {en},
	number = {2},
	urldate = {2025-07-04},
	journal = {Schizophr Bull},
	author = {DeLisi, Lynn E.},
	month = mar,
	year = {2008},
	note = {Publisher: Oxford Academic},
	pages = {312--321},
	file = {Full Text:/home/localaak22/snap/zotero-snap/common/Zotero/storage/K3QS6FV7/DeLisi - 2008 - The Concept of Progressive Brain Change in Schizophrenia Implications for Understanding Schizophren.pdf:application/pdf},
}

@article{fuhrmann_midpoint_2022,
	title = {The midpoint of cortical thinning between late childhood and early adulthood differs between individuals and brain regions: {Evidence} from longitudinal modelling in a 12-wave neuroimaging sample},
	volume = {261},
	issn = {1095-9572},
	shorttitle = {The midpoint of cortical thinning between late childhood and early adulthood differs between individuals and brain regions},
	doi = {10.1016/j.neuroimage.2022.119507},
	abstract = {Charting human brain maturation between childhood and adulthood is a fundamental prerequisite for understanding the rapid biological and psychological changes during human development. Two barriers have precluded the quantification of maturational trajectories: demands on data and demands on estimation. Using high-temporal resolution neuroimaging data of up to 12-waves in the HUBU cohort (N = 90, aged 7-21 years) we investigate changes in apparent cortical thickness across childhood and adolescence. Fitting a four-parameter logistic nonlinear random effects mixed model, we quantified the characteristic, s-shaped, trajectory of cortical thinning in adolescence. This approach yields biologically meaningful parameters, including the midpoint of cortical thinning (MCT), which corresponds to the age at which the cortex shows most rapid thinning - in our sample occurring, on average, at 14 years of age. These results show that, given suitable data and models, cortical maturation can be quantified with precision for each individual and brain region.},
	language = {eng},
	journal = {Neuroimage},
	author = {Fuhrmann, D. and Madsen, K. S. and Johansen, L. B. and Baaré, W. F. C. and Kievit, R. A.},
	month = nov,
	year = {2022},
	pmid = {35882270},
	keywords = {Adolescence, Adolescent, Adult, Brain, Brain maturation, Cerebral Cortex, Cerebral Cortical Thinning, Child, Cortical thickness, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Neuroimaging, Nonlinear mixed models, Rostral anterior cingulate, Rostral middle frontal gyrus, Sex differences},
	pages = {119507},
	file = {Full Text:/home/localaak22/snap/zotero-snap/common/Zotero/storage/EB9U7NIN/Fuhrmann et al. - 2022 - The midpoint of cortical thinning between late childhood and early adulthood differs between individ.pdf:application/pdf},
}

@article{mills_inter-individual_2021,
	title = {Inter-individual variability in structural brain development from late childhood to young adulthood},
	volume = {242},
	issn = {1053-8119},
	url = {https://www.sciencedirect.com/science/article/pii/S1053811921007242},
	doi = {10.1016/j.neuroimage.2021.118450},
	abstract = {A fundamental task in neuroscience is to characterize the brain's developmental course. While replicable group-level models of structural brain development from childhood to adulthood have recently been identified, we have yet to quantify and understand individual differences in structural brain development. The present study examined inter-individual variability and sex differences in changes in brain structure, as assessed by anatomical MRI, across ages 8.0–26.0 years in 269 participants (149 females) with three time points of data (807 scans), drawn from three longitudinal datasets collected in the Netherlands, Norway, and USA. We further investigated the relationship between overall brain size and developmental changes, as well as how females and males differed in change variability across development. There was considerable inter-individual variability in the magnitude of changes observed for all examined brain measures. The majority of individuals demonstrated decreases in total gray matter volume, cortex volume, mean cortical thickness, and white matter surface area in mid-adolescence, with more variability present during the transition into adolescence and the transition into early adulthood. While most individuals demonstrated increases in white matter volume in early adolescence, this shifted to a majority demonstrating stability starting in mid-to-late adolescence. We observed sex differences in these patterns, and also an association between the size of an individual's brain structure and the overall rate of change for the structure. The present study provides new insight as to the amount of individual variance in changes in structural morphometrics from late childhood to early adulthood in order to obtain a more nuanced picture of brain development. The observed individual- and sex-differences in brain changes also highlight the importance of further studying individual variation in developmental patterns in healthy, at-risk, and clinical populations.},
	urldate = {2025-07-04},
	journal = {NeuroImage},
	author = {Mills, Kathryn L. and Siegmund, Kimberly D. and Tamnes, Christian K. and Ferschmann, Lia and Wierenga, Lara M. and Bos, Marieke G. N. and Luna, Beatriz and Li, Chun and Herting, Megan M.},
	month = nov,
	year = {2021},
	keywords = {Adolescence, Cortex, Gray matter, Longitudinal, Subcortical, White matter},
	pages = {118450},
	file = {Full Text:/home/localaak22/snap/zotero-snap/common/Zotero/storage/48HKQ48S/Mills et al. - 2021 - Inter-individual variability in structural brain development from late childhood to young adulthood.pdf:application/pdf;ScienceDirect Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/SMXILWWW/S1053811921007242.html:text/html},
}

@article{tamnes_development_2017,
	title = {Development of the {Cerebral} {Cortex} across {Adolescence}: {A} {Multisample} {Study} of {Inter}-{Related} {Longitudinal} {Changes} in {Cortical} {Volume}, {Surface} {Area}, and {Thickness}},
	volume = {37},
	issn = {1529-2401},
	shorttitle = {Development of the {Cerebral} {Cortex} across {Adolescence}},
	doi = {10.1523/JNEUROSCI.3302-16.2017},
	abstract = {Before we can assess and interpret how developmental changes in human brain structure relate to cognition, affect, and motivation, and how these processes are perturbed in clinical or at-risk populations, we must first precisely understand typical brain development and how changes in different structural components relate to each other. We conducted a multisample magnetic resonance imaging study to investigate the development of cortical volume, surface area, and thickness, as well as their inter-relationships, from late childhood to early adulthood (7-29 years) using four separate longitudinal samples including 388 participants and 854 total scans. These independent datasets were processed and quality-controlled using the same methods, but analyzed separately to study the replicability of the results across sample and image-acquisition characteristics. The results consistently showed widespread and regionally variable nonlinear decreases in cortical volume and thickness and comparably smaller steady decreases in surface area. Further, the dominant contributor to cortical volume reductions during adolescence was thinning. Finally, complex regional and topological patterns of associations between changes in surface area and thickness were observed. Positive relationships were seen in sulcal regions in prefrontal and temporal cortices, while negative relationships were seen mainly in gyral regions in more posterior cortices. Collectively, these results help resolve previous inconsistencies regarding the structural development of the cerebral cortex from childhood to adulthood, and provide novel insight into how changes in the different dimensions of the cortex in this period of life are inter-related.SIGNIFICANCE STATEMENT Different measures of brain anatomy develop differently across adolescence. Their precise trajectories and how they relate to each other throughout development are important to know if we are to fully understand both typical development and disorders involving aberrant brain development. However, our understanding of such trajectories and relationships is still incomplete. To provide accurate characterizations of how different measures of cortical structure develop, we performed an MRI investigation across four independent datasets. The most profound anatomical change in the cortex during adolescence was thinning, with the largest decreases observed in the parietal lobe. There were complex regional patterns of associations between changes in surface area and thickness, with positive relationships seen in sulcal regions in prefrontal and temporal cortices, and negative relationships seen mainly in gyral regions in more posterior cortices.},
	language = {eng},
	number = {12},
	journal = {J Neurosci},
	author = {Tamnes, Christian K. and Herting, Megan M. and Goddings, Anne-Lise and Meuwese, Rosa and Blakemore, Sarah-Jayne and Dahl, Ronald E. and Güroğlu, Berna and Raznahan, Armin and Sowell, Elizabeth R. and Crone, Eveline A. and Mills, Kathryn L.},
	month = mar,
	year = {2017},
	pmid = {28242797},
	pmcid = {PMC5373125},
	keywords = {Adolescent, Adult, Aging, brain development, Cerebral Cortex, Child, Female, gray matter, Humans, Imaging, Three-Dimensional, Longitudinal Studies, Magnetic Resonance Imaging, Male, morphometry, MRI, Organ Size, replication, Reproducibility of Results, Sensitivity and Specificity, Young Adult},
	pages = {3402--3412},
	file = {Full Text:/home/localaak22/snap/zotero-snap/common/Zotero/storage/H3V9MLDM/Tamnes et al. - 2017 - Development of the Cerebral Cortex across Adolescence A Multisample Study of Inter-Related Longitud.pdf:application/pdf},
}

@article{wolfers_individual_nodate,
	title = {Individual differences v. the average patient: mapping the heterogeneity in {ADHD} using normative models},
	volume = {50},
	issn = {0033-2917},
	shorttitle = {Individual differences v. the average patient},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7083555/},
	doi = {10.1017/S0033291719000084},
	abstract = {Background
The present paper presents a fundamentally novel approach to model individual differences of persons with the same biologically heterogeneous mental disorder. Unlike prevalent case-control analyses, that assume a clear distinction between patient and control groups and thereby introducing the concept of an ‘average patient’, we describe each patient's biology individually, gaining insights into the different facets that characterize persistent attention-deficit/hyperactivity disorder (ADHD).

Methods
Using a normative modeling approach, we mapped inter-individual differences in reference to normative structural brain changes across the lifespan to examine the degree to which case-control analyses disguise differences between individuals.

Results
At the level of the individual, deviations from the normative model were frequent in persistent ADHD. However, the overlap of more than 2\% between participants with ADHD was only observed in few brain loci. On average, participants with ADHD showed significantly reduced gray matter in the cerebellum and hippocampus compared to healthy individuals. While the case-control differences were in line with the literature on ADHD, individuals with ADHD only marginally reflected these group differences.

Conclusions
Case-control comparisons, disguise inter-individual differences in brain biology in individuals with persistent ADHD. The present results show that the ‘average ADHD patient’ has limited informative value, providing the first evidence for the necessity to explore different biological facets of ADHD at the level of the individual and practical means to achieve this end.},
	number = {2},
	urldate = {2025-07-04},
	journal = {Psychol Med},
	author = {Wolfers, Thomas and Beckmann, Christian F. and Hoogman, Martine and Buitelaar, Jan K. and Franke, Barbara and Marquand, Andre F.},
	pmid = {30782224},
	pmcid = {PMC7083555},
	pages = {314--323},
	file = {Full Text:/home/localaak22/snap/zotero-snap/common/Zotero/storage/ZCXYJBYM/Wolfers et al. - Individual differences v. the average patient mapping the heterogeneity in ADHD using normative mod.pdf:application/pdf},
}

@misc{noauthor_zabihi_nodate,
	title = {Zabihi {M}, {Oldehinkel} {M}, {Wolfers} {T}, {Frouin} {V}, {Goyard} {D}, {Loth} {E}, et al. {Dissecting} the heterogeneous cortical anatomy of autism spectrum disorder using normative models. {Biol} {Psychiatry} {Cogn} {Neurosci} {Neuroimaging}. 2018. - {Google} {Search}},
	url = {https://www.google.com/search?q=Zabihi+M%2C+Oldehinkel+M%2C+Wolfers+T%2C+Frouin+V%2C+Goyard+D%2C+Loth+E%2C+et+al.+Dissecting+the+heterogeneous+cortical+anatomy+of+autism+spectrum+disorder+using+normative+models.+Biol+Psychiatry+Cogn+Neurosci+Neuroimaging.+2018.&oq=Zabihi+M%2C+Oldehinkel+M%2C+Wolfers+T%2C+Frouin+V%2C+Goyard+D%2C+Loth+E%2C+et+al.+Dissecting+the+heterogeneous+cortical+anatomy+of+autism+spectrum+disorder+using+normative+models.+Biol+Psychiatry+Cogn+Neurosci+Neuroimaging.+2018.&gs_lcrp=EgZjaHJvbWUyBggAEEUYOdIBBzM0NmowajSoAgCwAgE&sourceid=chrome&ie=UTF-8},
	urldate = {2025-07-04},
}

@article{zabihi_dissecting_2019,
	title = {Dissecting the {Heterogeneous} {Cortical} {Anatomy} of {Autism} {Spectrum} {Disorder} {Using} {Normative} {Models}},
	volume = {4},
	issn = {2451-9030},
	doi = {10.1016/j.bpsc.2018.11.013},
	abstract = {BACKGROUND: The neuroanatomical basis of autism spectrum disorder (ASD) has remained elusive, mostly owing to high biological and clinical heterogeneity among diagnosed individuals. Despite considerable effort toward understanding ASD using neuroimaging biomarkers, heterogeneity remains a barrier, partly because studies mostly employ case-control approaches, which assume that the clinical group is homogeneous.
METHODS: Here, we used an innovative normative modeling approach to parse biological heterogeneity in ASD. We aimed to dissect the neuroanatomy of ASD by mapping the deviations from a typical pattern of neuroanatomical development at the level of the individual and to show the necessity to look beyond the case-control paradigm to understand the neurobiology of ASD. We first estimated a vertexwise normative model of cortical thickness development using Gaussian process regression, then mapped the deviation of each participant from the typical pattern. For this, we employed a heterogeneous cross-sectional sample of 206 typically developing individuals (127 males) and 321 individuals with ASD (232 males) (6-31 years of age).
RESULTS: We found few case-control differences, but the ASD cohort showed highly individualized patterns of deviations in cortical thickness that were widespread across the brain. These deviations correlated with severity of repetitive behaviors and social communicative symptoms, although only repetitive behaviors survived corrections for multiple testing.
CONCLUSIONS: Our results 1) reinforce the notion that individuals with ASD show distinct, highly individualized trajectories of brain development and 2) show that by focusing on common effects (i.e., the "average ASD participant"), the case-control approach disguises considerable interindividual variation crucial for precision medicine.},
	language = {eng},
	number = {6},
	journal = {Biol Psychiatry Cogn Neurosci Neuroimaging},
	author = {Zabihi, Mariam and Oldehinkel, Marianne and Wolfers, Thomas and Frouin, Vincent and Goyard, David and Loth, Eva and Charman, Tony and Tillmann, Julian and Banaschewski, Tobias and Dumas, Guillaume and Holt, Rosemary and Baron-Cohen, Simon and Durston, Sarah and Bölte, Sven and Murphy, Declan and Ecker, Christine and Buitelaar, Jan K. and Beckmann, Christian F. and Marquand, Andre F.},
	month = jun,
	year = {2019},
	pmid = {30799285},
	pmcid = {PMC6551348},
	keywords = {Adolescent, Adult, Autism, Autism Spectrum Disorder, Case-Control Studies, Cerebral Cortex, Child, Cortical thickness, Cross-Sectional Studies, Female, Gaussian process, Heterogeneity, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Models, Neurological, Normative modeling, Outlier detection, Young Adult},
	pages = {567--578},
	file = {Full Text:/home/localaak22/snap/zotero-snap/common/Zotero/storage/2JECEG7A/Zabihi et al. - 2019 - Dissecting the Heterogeneous Cortical Anatomy of Autism Spectrum Disorder Using Normative Models.pdf:application/pdf},
}



@article{fu_novel_2021,
	title = {A novel pipeline leveraging surface-based features of small subcortical structures to classify individuals with autism spectrum disorder},
	volume = {104},
	issn = {0278-5846},
	url = {https://www.sciencedirect.com/science/article/pii/S0278584620303055},
	doi = {10.1016/j.pnpbp.2020.109989},
	abstract = {Autism spectrum disorder (ASD) is accompanied with widespread impairment in social-emotional functioning. Classification of ASD using sensitive morphological features derived from structural magnetic resonance imaging (MRI) of the brain may help us to better understand ASD-related mechanisms and improve related automatic diagnosis. Previous studies using T1 MRI scans in large heterogeneous ABIDE dataset with typical development (TD) controls reported poor classification accuracies (around 60\%). This may because they only considered surface-based morphometry (SBM) as scalar estimates (such as cortical thickness and surface area) and ignored the neighboring intrinsic geometry information among features. In recent years, the shape-related SBM achieves great success in discovering the disease burden and progression of other brain diseases. However, when focusing on local geometry information, its high dimensionality requires careful treatment in its application to machine learning. To address the above challenges, we propose a novel pipeline for ASD classification, which mainly includes the generation of surface-based features, patch-based surface sparse coding and dictionary learning, Max-pooling and ensemble classifiers based on adaptive optimizers. The proposed pipeline may leverage the sensitivity of brain surface morphometry statistics and the efficiency of sparse coding and Max-pooling. By introducing only the surface features of bilateral hippocampus that derived from 364 male subjects with ASD and 381 age-matched TD males, this pipeline outperformed five recent MRI-based ASD classification studies with {\textgreater}80\% accuracy in discriminating individuals with ASD from TD controls. Our results suggest shape-related SBM features may further boost the classification performance of MRI between ASD and TD.},
	urldate = {2025-07-04},
	journal = {Progress in Neuro-Psychopharmacology and Biological Psychiatry},
	author = {Fu, Yu and Zhang, Jie and Li, Yuan and Shi, Jie and Zou, Ying and Guo, Hanning and Li, Yongchao and Yao, Zhijun and Wang, Yalin and Hu, Bin},
	month = jan,
	year = {2021},
	keywords = {Autism spectrum disorder (ASD), Classification, High-dimensional features, Surface-based morphometry (SBM)},
	pages = {109989},
	file = {ScienceDirect Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/4RHTFVPM/Fu et al. - 2021 - A novel pipeline leveraging surface-based features of small subcortical structures to classify indiv.pdf:application/pdf;ScienceDirect Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/28IM9J5B/S0278584620303055.html:text/html},
}

@article{ashburner_voxel-based_2000,
	title = {Voxel-based morphometry--the methods},
	volume = {11},
	issn = {1053-8119},
	doi = {10.1006/nimg.2000.0582},
	abstract = {At its simplest, voxel-based morphometry (VBM) involves a voxel-wise comparison of the local concentration of gray matter between two groups of subjects. The procedure is relatively straightforward and involves spatially normalizing high-resolution images from all the subjects in the study into the same stereotactic space. This is followed by segmenting the gray matter from the spatially normalized images and smoothing the gray-matter segments. Voxel-wise parametric statistical tests which compare the smoothed gray-matter images from the two groups are performed. Corrections for multiple comparisons are made using the theory of Gaussian random fields. This paper describes the steps involved in VBM, with particular emphasis on segmenting gray matter from MR images with nonuniformity artifact. We provide evaluations of the assumptions that underpin the method, including the accuracy of the segmentation and the assumptions made about the statistical distribution of the data.},
	language = {eng},
	number = {6 Pt 1},
	journal = {Neuroimage},
	author = {Ashburner, J. and Friston, K. J.},
	month = jun,
	year = {2000},
	pmid = {10860804},
	keywords = {Brain, False Positive Reactions, Humans, Magnetic Resonance Imaging, Models, Neurological, Periaqueductal Gray, Random Allocation},
	pages = {805--821},
	file = {Submitted Version:/home/localaak22/snap/zotero-snap/common/Zotero/storage/UCI8ILDX/Ashburner and Friston - 2000 - Voxel-based morphometry--the methods.pdf:application/pdf},
}

@article{farbota_longitudinal_2012,
	title = {{LONGITUDINAL} {VOLUMETRIC} {CHANGES} {FOLLOWING} {TRAUMATIC} {BRAIN} {INJURY}: {A} {TENSOR} {BASED} {MORPHOMETRY} {STUDY}},
	volume = {18},
	issn = {1355-6177},
	shorttitle = {{LONGITUDINAL} {VOLUMETRIC} {CHANGES} {FOLLOWING} {TRAUMATIC} {BRAIN} {INJURY}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3571099/},
	doi = {10.1017/S1355617712000835},
	abstract = {After traumatic injury the brain undergoes a prolonged period of degenerative change that is paradoxically accompanied by cognitive recovery. The spatiotemporal pattern of atrophy and the specific relationships of atrophy to cognitive changes are ill understood. The present study used tensor based morphometry and neuropsychological testing to examine brain volume loss in 17 TBI patients and 13 controls over a four year period. Patients were scanned at two months, one year and four years post-injury. High-dimensional warping procedures were used to create change maps of each subject’s brain for each of the two intervals. TBI patients experienced volume loss in both cortical areas and white matter regions during the first interval. We also observed continuing volume loss in extensive regions of white matter during the second interval. Neuropsychological correlations indicated that cognitive tasks were associated with subsequent volume loss in task-relevant regions. The extensive volume loss in brain white matter observed well beyond the first year post-injury suggests that the injured brain remains malleable for an extended period, and the neuropsychological relationships suggest that this volume loss may be associated with subtle cognitive improvements.},
	number = {6},
	urldate = {2025-07-04},
	journal = {J Int Neuropsychol Soc},
	author = {Farbota, Kimberly D. M. and Sodhi, Aparna and Bendlin, Barbara B. and McLaren, Donald G. and Xu, Guofan and Rowley, Howard A. and Johnson, Sterling C.},
	month = nov,
	year = {2012},
	pmid = {22883443},
	pmcid = {PMC3571099},
	pages = {1006--1018},
	file = {PubMed Central Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/E2BTHYUB/Farbota et al. - 2012 - LONGITUDINAL VOLUMETRIC CHANGES FOLLOWING TRAUMATIC BRAIN INJURY A TENSOR BASED MORPHOMETRY STUDY.pdf:application/pdf},
}

@article{chen_structural_2011,
	title = {Structural {MRI} in {Autism} {Spectrum} {Disorder}},
	volume = {69},
	copyright = {2011 International Pediatrics Research Foundation, Inc.},
	issn = {1530-0447},
	url = {https://www.nature.com/articles/pr9201195},
	doi = {10.1203/PDR.0b013e318212c2b3},
	abstract = {Magnetic resonance (MR) examination provides a powerful tool for investigating brain structural changes in children with autism spectrum disorder (ASD). We review recent advances in the understanding of structural MR correlates of ASD. We summarize findings from studies based on voxel-based morphometry, surface-based morphometry, tensor-based morphometry, and diffusion-tensor imaging. Finally, we discuss diagnostic models of ASD based on MR-derived features.},
	language = {en},
	number = {8},
	urldate = {2025-07-04},
	journal = {Pediatr Res},
	author = {Chen, Rong and Jiao, Yun and Herskovits, Edward H.},
	month = may,
	year = {2011},
	note = {Publisher: Nature Publishing Group},
	keywords = {Autism spectrum disorders, Magnetic resonance imaging, Paediatrics},
	pages = {63--68},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/PKPQUDLT/Chen et al. - 2011 - Structural MRI in Autism Spectrum Disorder.pdf:application/pdf},
}

@article{fu_novel_2021-1,
	title = {A novel pipeline leveraging surface-based features of small subcortical structures to classify individuals with autism spectrum disorder},
	volume = {104},
	issn = {0278-5846},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9632410/},
	doi = {10.1016/j.pnpbp.2020.109989},
	abstract = {Autism spectrum disorder (ASD) is accompanied with widespread impairment in social-emotional functioning. Classification of ASD using sensitive morphological features derived from structural magnetic resonance imaging (MRI) of the brain may help us to better understand ASD-related mechanisms and improve related automatic diagnosis. Previous studies using T1 MRI scans in large heterogeneous ABIDE dataset with typical development (TD) controls reported poor classification accuracies (around 60\%). This may because they only considered surface-based morphometry (SBM) as scalar estimates (such as cortical thickness and surface area) and ignored the neighboring intrinsic geometry information among features. In recent years, the shape-related SBM achieves great success in discovering the disease burden and progression of other brain diseases. However, when focusing on local geometry information, its high dimensionality requires careful treatment in its application to machine learning. To address the above challenges, we propose a novel pipeline for ASD classification, which mainly includes the generation of surface-based features, patch-based surface sparse coding and dictionary learning, Max-pooling and ensemble classifiers based on adaptive optimizers. The proposed pipeline may leverage the sensitivity of brain surface morphometry statistics and the efficiency of sparse coding and Max-pooling. By introducing only the surface features of bilateral hippocampus that derived from 364 male subjects with ASD and 381 age-matched TD males, this pipeline outperformed five recent MRI-based ASD classification studies with {\textgreater} 80\% accuracy in discriminating individuals with ASD from TD controls. Our results suggest shape-related SBM features may further boost the classification performance of MRI between ASD and TD.},
	urldate = {2025-07-04},
	journal = {Prog Neuropsychopharmacol Biol Psychiatry},
	author = {Fu, Yu and Zhang, Jie and Li, Yuan and Shi, Jie and Zou, Ying and Guo, Hanning and Li, Yongchao and Yao, Zhijun and Wang, Yalin and Hu, Bin},
	month = jan,
	year = {2021},
	pmid = {32512131},
	pmcid = {PMC9632410},
	pages = {109989},
	file = {PubMed Central Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/ICC4IRHA/Fu et al. - 2021 - A novel pipeline leveraging surface-based features of small subcortical structures to classify indiv.pdf:application/pdf},
}

@article{khan_unified_2015,
	title = {Unified voxel- and tensor-based morphometry ({UVTBM}) using registration confidence},
	volume = {36 Suppl 1},
	issn = {1558-1497},
	doi = {10.1016/j.neurobiolaging.2014.04.036},
	abstract = {Voxel-based morphometry (VBM) and tensor-based morphometry (TBM) both rely on spatial normalization to a template and yet have different requirements for the level of registration accuracy. VBM requires only global alignment of brain structures, with limited degrees of freedom in transformation, whereas TBM performs best when the registration is highly deformable and can achieve higher registration accuracy. In addition, the registration accuracy varies over the whole brain, with higher accuracy typically observed in subcortical areas and lower accuracy seen in cortical areas. Hence, even the determinant of Jacobian of registration maps is spatially varying in their accuracy, and combining these with VBM by direct multiplication introduces errors in VBM maps where the registration is inaccurate. We propose a unified approach to combining these 2 morphometry methods that is motivated by these differing requirements for registration and our interest in harnessing the advantages of both. Our novel method uses local estimates of registration confidence to determine how to weight the influence of VBM- and TBM-like approaches. Results are shown on healthy and mild Alzheimer's subjects (N = 150) investigating age and group differences, and potential of differential diagnosis is shown on a set of Alzheimer's disease (N = 34) and frontotemporal dementia (N = 30) patients compared against controls (N = 14). These show that the group differences detected by our proposed approach are more descriptive than those detected from VBM, Jacobian-modulated VBM, and TBM separately, hence leveraging the advantages of both approaches in a unified framework.},
	language = {eng},
	number = {Suppl 1},
	journal = {Neurobiol Aging},
	author = {Khan, Ali R. and Wang, Lei and Beg, Mirza Faisal},
	month = jan,
	year = {2015},
	pmid = {25264347},
	pmcid = {PMC8026273},
	keywords = {Alzheimer Disease, Atlases, Brain, Brain registration, Diagnosis, Differential, Diffeomorphisms, Female, Frontotemporal Dementia, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Morphometry, MRI, Neurodegenerative Diseases, Neuroimaging, Tensor-based morphometry, Voxel-based morphometry},
	pages = {S60--68},
}

@article{rommel_association_2017,
	title = {Association of {Preterm} {Birth} {With} {Attention}-{Deficit}/{Hyperactivity} {Disorder}–{Like} and {Wider}-{Ranging} {Neurophysiological} {Impairments} of {Attention} and {Inhibition}},
	volume = {56},
	issn = {0890-8567, 1527-5418},
	url = {https://www.jaacap.org/article/S0890-8567(16)31854-8/fulltext},
	doi = {10.1016/j.jaac.2016.10.006},
	language = {English},
	number = {1},
	urldate = {2025-07-04},
	journal = {Journal of the American Academy of Child \& Adolescent Psychiatry},
	author = {Rommel, Anna-Sophie and James, Sarah-Naomi and McLoughlin, Gráinne and Brandeis, Daniel and Banaschewski, Tobias and Asherson, Philip and Kuntsi, Jonna},
	month = jan,
	year = {2017},
	pmid = {27993227},
	note = {Publisher: Elsevier},
	keywords = {ADHD, EEG, event-related potential, neurocognitive impairment, preterm birth},
	pages = {40--50},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/QMJAIGTC/Rommel et al. - 2017 - Association of Preterm Birth With Attention-DeficitHyperactivity Disorder–Like and Wider-Ranging Ne.pdf:application/pdf},
}

@article{hee_chung_neurodevelopmental_2020,
	title = {Neurodevelopmental outcomes of preterm infants: a recent literature review},
	volume = {9},
	issn = {2224-4344},
	shorttitle = {Neurodevelopmental outcomes of preterm infants},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7082240/},
	doi = {10.21037/tp.2019.09.10},
	abstract = {Background
Over the past several decades, improvements in technology in the Neonatal Intensive Care Unit (NICU) have led to improved survival of preterm infants. Some studies have found that premature infants are at higher risk of behavioral problems, motor and sensory abnormalities, developmental delay, and poorer academic performance, while other studies have found no significant difference.

Methods
A literature search was conducted through PubMed for articles published between January 2018 and September 2019. Studies that concentrated on preterm infants with relatively uncomplicated NICU courses and without extensive medical interventions were selected.

Results
Historically, preterm infants have been found to be at increased risk for the inattentive subtype of attention deficit hyperactivity disorder (ADHD), depression, anxiety, autism spectrum disorder (ASD), avoidant personality, and anti-social personality, when compared to full term infants. However, some studies found that this difference between the two groups decrease as they enter adolescence and adulthood. Preterm infants are at increased risk for language, cognitive, sensory and motor deficits. Greater gestational age (GA) at birth and higher birth weight is associated with a lower risk of developmental delay. Cohort studies focusing on motor development showed that the degree of impairment decreased over time. Adverse childhood experiences (ACEs) have a negative correlation on multiple domains of development. The overall outcome of these infants may be influenced by socioeconomic status (SES), neonatal morbidities, demographics and parental education. Hearing and vision deficits are relatively infrequent among premature infants. A significant risk factor for hearing impairment involves the use of ototoxic agents such as gentamicin and infants with a patent ductus arteriosus (PDA).

Conclusions
Preterm infants are at higher risk of adverse neurodevelopmental outcomes when compared to their full-term counter parts. However, in recent years it appears that rates of certain neurologic and developmental conditions are occurring in rates lower than historically noted. Premature individuals with possible developmental or mental health concerns should be identified early on so that interventions can be implemented immediately. Those meeting developmental milestone should continue to be monitored closely as deficits may develop later.},
	number = {Suppl 1},
	urldate = {2025-07-04},
	journal = {Transl Pediatr},
	author = {Hee Chung, Estefani and Chou, Jesse and Brown, Kelly A.},
	month = feb,
	year = {2020},
	pmid = {32206579},
	pmcid = {PMC7082240},
	pages = {S3--S8},
	file = {Full Text:/home/localaak22/snap/zotero-snap/common/Zotero/storage/ZR8GCUTH/Hee Chung et al. - 2020 - Neurodevelopmental outcomes of preterm infants a recent literature review.pdf:application/pdf},
}



@article{bonthrone_cortical_2025,
	title = {Cortical scaling of the neonatal brain in typical and altered development},
	volume = {122},
	url = {https://www.pnas.org/doi/10.1073/pnas.2416423122},
	doi = {10.1073/pnas.2416423122},
	abstract = {Theoretically derived scaling laws capture the nonlinear relationships between rapidly expanding brain volume and cortical gyrification across mammalian species and in adult humans. However, the preservation of these laws has not been comprehensively assessed in typical or pathological brain development. Here, we assessed the scaling laws governing cortical thickness (CT), surface area (SA), and cortical folding in the neonatal brain. We also assessed multivariate morphological terms that capture brain size, shape, and folding processes. The sample consisted of 345 typically developing infants, 73 preterm infants, and 107 infants with congenital heart disease (CHD) who underwent brain MRI. Our results show that typically developing neonates and those with CHD follow the cortical folding scaling law obtained from mammalian brains, children, and adults which captures the relationship between exposed SA, total SA, and CT. Cortical folding scaling was not affected by gestational age at birth, postmenstrual age at scan, sex, or multiple birth in these populations. CHD was characterized by a unique reduction in the multivariate morphological term capturing size, suggesting that CHD affects cortical growth overall but not cortical folding processes. In contrast, preterm birth was characterized by altered cortical folding scaling and altered shape, suggesting that the developmentally programmed processes of cortical folding are disrupted in this population. The degree of altered shape was associated with cognitive abilities in early childhood in preterm infants.},
	number = {15},
	urldate = {2025-07-06},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Bonthrone, Alexandra F. and Cromb, Daniel and Chew, Andrew and Gal-Er, Barat and Kelly, Christopher and Falconer, Shona and Arichi, Tomoki and Pushparajah, Kuberan and Simpson, John and Rutherford, Mary A. and Hajnal, Joseph V. and Nosarti, Chiara and Edwards, A. David and O’Muircheartaigh, Jonathan and Counsell, Serena J.},
	month = apr,
	year = {2025},
	note = {Publisher: Proceedings of the National Academy of Sciences},
	pages = {e2416423122},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/5GJFT9IP/Bonthrone et al. - 2025 - Cortical scaling of the neonatal brain in typical and altered development.pdf:application/pdf},
}

@article{ciceri_fetal_2024,
	title = {Fetal gestational age prediction via shape descriptors of cortical development},
	volume = {12},
	issn = {2296-2360},
	url = {https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2024.1471080/full},
	doi = {10.3389/fped.2024.1471080},
	abstract = {Introduction
Gyrification is the intricate process through which the mammalian cerebral cortex develops its characteristic pattern of sulci and gyri. Monitoring gyrification provides valuable insights into brain development and identifies potential abnormalities at an early stage. This study analyzes the cortical structure in neurotypical and pathological (spina bifida) fetuses using various shape descriptors to shed light on the gyrification process during pregnancy.

Methods
We compare morphometric properties encoded by commonly used scalar point-wise curvature-based signatures—such as mean curvature (H), Gaussian curvature (K), shape index (SI), and curvedness (C)—with multidimensional point-wise shape signatures, including spectral geometry processing methods like the Heat Kernel Signature (HKS) and Wave Kernel Signature (WKS), as well as the Signature of Histograms of Orientations (SHOT), which combines histogram and signature techniques. These latter signatures originate from computer graphics techniques and are rarely applied in the medical field. We propose a novel technique to derive a global descriptor from a given point-wise signature, obtaining GHKS, GWKS, and GSHOT. The extracted signatures are then evaluated using Support Vector Regression (SVR)-based algorithms to predict fetal gestational age (GA).

Results
GSHOT better encodes the GA to other global multidimensional point-wise shape signatures (GHKS, GWKS) and commonly used scalar point-wise curvature-based signatures (C, H, K, SI, FI), achieving a prediction R2 of 0.89 and a mean absolute error of 6 days in neurotypical fetuses, and a R2 of 0.64 and a mean absolute error of 10 days in pathological fetuses.

Conclusion
GSHOT provides researchers with an advanced tool to capture more nuanced aspects of fetal brain development and, specifically, of the gyrification process.},
	language = {English},
	urldate = {2025-07-06},
	journal = {Front. Pediatr.},
	author = {Ciceri, Tommaso and Squarcina, Letizia and Bertoldo, Alessandra and Brambilla, Paolo and Melzi, Simone and Peruzzo, Denis},
	month = nov,
	year = {2024},
	note = {Publisher: Frontiers},
	keywords = {cortical surface, fetal brain, Gestational Age prediction, MRI, shape descriptors},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/VTXWW87H/Ciceri et al. - 2024 - Fetal gestational age prediction via shape descriptors of cortical development.pdf:application/pdf},
}

@article{blinkouskaya_brain_2021,
	title = {Brain {Shape} {Changes} {Associated} {With} {Cerebral} {Atrophy} in {Healthy} {Aging} and {Alzheimer}’s {Disease}},
	volume = {7},
	issn = {2297-3079},
	url = {https://www.frontiersin.org/journals/mechanical-engineering/articles/10.3389/fmech.2021.705653/full},
	doi = {10.3389/fmech.2021.705653},
	abstract = {Both healthy and pathological brain aging are characterized by various degrees of cognitive decline that strongly correlate with morphological changes referred to as cerebral atrophy. These hallmark morphological changes include cortical thinning, white and gray matter volume loss, ventricular enlargement, and loss of gyrification all caused by a myriad of subcellular and cellular aging processes. While the biology of brain aging has been investigated extensively, the mechanics of brain aging remains vastly understudied. Here, we propose a multiphysics model that couples tissue atrophy and Alzheimer’s disease biomarker progression. We adopt the multiplicative split of the deformation gradient into a shrinking and an elastic part. We model atrophy as region-specific isotropic shrinking and differentiate between a constant, tissue-dependent atrophy rate in healthy aging, and an atrophy rate in Alzheimer’s disease that is proportional to the local biomarker concentration. Our finite element modeling approach delivers a computational framework to systematically study the spatiotemporal progression of cerebral atrophy and its regional effect on brain shape. We verify our results via comparison with cross-sectional medical imaging studies that reveal persistent age-related atrophy patterns. Our long-term goal is to develop a diagnostic tool able to differentiate between healthy and accelerated aging, typically observed in Alzheimer’s disease and related dementias, in order to allow for earlier and more effective interventions.},
	language = {English},
	urldate = {2025-07-06},
	journal = {Front. Mech. Eng.},
	author = {Blinkouskaya, Yana and Weickenmeier, Johannes},
	month = jul,
	year = {2021},
	note = {Publisher: Frontiers},
	keywords = {Alzheimer's disease, Brain aging, brain shape changes, cerebral atrophy, Finite Element Modeling, Multiphysics modeling},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/JCBKQ5XU/Blinkouskaya and Weickenmeier - 2021 - Brain Shape Changes Associated With Cerebral Atrophy in Healthy Aging and Alzheimer’s Disease.pdf:application/pdf},
}


@misc{jian_timeflow_2025,
	title = {{TimeFlow}: {Longitudinal} {Brain} {Image} {Registration} and {Aging} {Progression} {Analysis}},
	shorttitle = {{TimeFlow}},
	url = {http://arxiv.org/abs/2501.08667},
	doi = {10.48550/arXiv.2501.08667},
	abstract = {Predicting future brain states is crucial for understanding healthy aging and neurodegenerative diseases. Longitudinal brain MRI registration, a cornerstone for such analyses, has long been limited by its inability to forecast future developments, reliance on extensive, dense longitudinal data, and the need to balance registration accuracy with temporal smoothness. In this work, we present {\textbackslash}emph\{TimeFlow\}, a novel framework for longitudinal brain MRI registration that overcomes all these challenges. Leveraging a U-Net architecture with temporal conditioning inspired by diffusion models, TimeFlow enables accurate longitudinal registration and facilitates prospective analyses through future image prediction. Unlike traditional methods that depend on explicit smoothness regularizers and dense sequential data, TimeFlow achieves temporal consistency and continuity without these constraints. Experimental results highlight its superior performance in both future timepoint prediction and registration accuracy compared to state-of-the-art methods. Additionally, TimeFlow supports novel biological brain aging analyses, effectively differentiating neurodegenerative conditions from healthy aging. It eliminates the need for segmentation, thereby avoiding the challenges of non-trivial annotation and inconsistent segmentation errors. TimeFlow paves the way for accurate, data-efficient, and annotation-free prospective analyses of brain aging and chronic diseases.},
	urldate = {2025-07-06},
	publisher = {arXiv},
	author = {Jian, Bailiang and Pan, Jiazhen and Li, Yitong and Bongratz, Fabian and Li, Ruochen and Rueckert, Daniel and Wiestler, Benedikt and Wachinger, Christian},
	month = jan,
	year = {2025},
	note = {arXiv:2501.08667 [eess]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/L7GYTAG4/Jian et al. - 2025 - TimeFlow Longitudinal Brain Image Registration and Aging Progression Analysis.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/7I36C2M9/2501.html:text/html},
}



@article{le_nonlinear_2018,
	title = {A {Nonlinear} {Simulation} {Framework} {Supports} {Adjusting} for {Age} {When} {Analyzing} {BrainAGE}},
	volume = {10},
	issn = {1663-4365},
	url = {https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2018.00317/full},
	doi = {10.3389/fnagi.2018.00317},
	abstract = {Several imaging modalities, including T1-weighted structural imaging, diffusion tensor imaging, and functional MRI can show chronological age related changes. Employing machine learning algorithms, an individual’s imaging data can predict their age with reasonable accuracy. While details vary according to modality, the general strategy is to: 1) extract image-related features, 2) build a model on a training set that uses those features to predict an individual’s age, 3) validate the model on a test dataset, producing a predicted age for each individual, 4) define the “Brain Age Gap Estimate” (BrainAGE) as the difference between an individual’s predicted age and his/her chronological age, and 5) estimate the relationship between BrainAGE and other variables of interest, and 6) make inferences about those variables and accelerated or delayed brain aging. For example, a group of individuals with overall positive BrainAGE may show signs of accelerated aging in other variables as well. There is inevitably an overestimation of the age of younger individuals and an underestimation of the age of older individuals due to ‘regression to the mean’. The correlation between chronological age and BrainAGE may significantly impact the relationship between BrainAGE and other variables of interest when they are also related to age. In this study, we examine the detectability of variable effects under different assumptions. We use empirical results from two separate datasets [training=475 healthy volunteers, aged 18 – 60 years (259 female); testing=489 participants including people with mood/anxiety, substance use, eating disorders and healthy controls, aged 18 – 56 years (312 female)] to inform simulation parameter selection. Outcomes in simulated and empirical data strongly support the proposal that models incorporating BrainAGE should include chronological age as a covariate. We propose either including age as a covariate in step 5 of the above framework, or employing a multistep procedure where age is regressed on BrainAGE prior to step 5, producing BrainAGE Residualized (BrainAGER) scores.},
	language = {English},
	urldate = {2025-07-06},
	journal = {Front. Aging Neurosci.},
	author = {Le, Trang T. and Kuplicki, Rayus T. and McKinney, Brett A. and Yeh, Hung-Wen and Thompson, Wesley K. and Paulus, Martin P. and Tulsa 1000 Investigators and Aupperle, Robin L. and Bodurka, Jerzy and Cha, Yoon-Hee and Feinstein, Justin S. and Khalsa, Sahib S. and Savitz, Jonathan and Simmons, W. Kyle and Victor, Teresa A.},
	month = oct,
	year = {2018},
	note = {Publisher: Frontiers},
	keywords = {Aging, BrainAGE, False positives, MRI, simulation, SVR},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/F9KKB7WC/Le et al. - 2018 - A Nonlinear Simulation Framework Supports Adjusting for Age When Analyzing BrainAGE.pdf:application/pdf},
}

@article{liang_assessing_2022,
	title = {Assessing brain and biological aging trajectories associated with {Alzheimer}’s disease},
	volume = {16},
	issn = {1662-453X},
	url = {https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.1036102/full},
	doi = {10.3389/fnins.2022.1036102},
	abstract = {The development of effective treatments to prevent and slow Alzheimer’s disease (AD) pathogenesis is needed in order to tackle the steady increase in the global prevalence of AD. This challenge is complicated by the need to identify key health shifts that precede the onset of AD and cognitive decline as these represent windows of opportunity for intervening and preventing disease. Such shifts may be captured through the measurement of biomarkers that reflect the health of the individual, in particular those that reflect brain age and biological age. Brain age biomarkers provide a composite view of the health of the brain based on neuroanatomical analyses, while biological age biomarkers, which encompass the epigenetic clock, provide a measurement of the overall health state of an individual based on DNA methylation analysis. Acceleration of brain and biological ages is associated with changes in cognitive function, as well as neuropathological markers of AD. In this mini-review, we discuss brain age and biological age research in the context of cognitive decline and AD. While more research is needed, studies show that brain and biological aging trajectories are variable across individuals and that such trajectories are non-linear at older ages. Longitudinal monitoring of these biomarkers may be valuable for enabling earlier identification of divergent pathological trajectories towards AD and providing insight into points for intervention.},
	language = {English},
	urldate = {2025-07-06},
	journal = {Front. Neurosci.},
	author = {Liang, Winnie S. and Goetz, Laura H. and Schork, Nicholas J.},
	month = oct,
	year = {2022},
	note = {Publisher: Frontiers},
	keywords = {Aging biomarker, Alzheimer's ​disease, biological age, Biological age (BA) estimation, Brain age, Brain age estimation, Epigenetic age, Epigenetic age acceleration, epigenetic clock},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/9Q5TMRXT/Liang et al. - 2022 - Assessing brain and biological aging trajectories associated with Alzheimer’s disease.pdf:application/pdf},
}

@article{cumplido-mayoral_biological_2023,
	title = {Biological brain age prediction using machine learning on structural neuroimaging data: {Multi}-cohort validation against biomarkers of {Alzheimer}’s disease and neurodegeneration stratified by sex},
	volume = {12},
	issn = {2050-084X},
	shorttitle = {Biological brain age prediction using machine learning on structural neuroimaging data},
	url = {https://doi.org/10.7554/eLife.81067},
	doi = {10.7554/eLife.81067},
	abstract = {Brain-age can be inferred from structural neuroimaging and compared to chronological age (brain-age delta) as a marker of biological brain aging. Accelerated aging has been found in neurodegenerative disorders like Alzheimer’s disease (AD), but its validation against markers of neurodegeneration and AD is lacking. Here, imaging-derived measures from the UK Biobank dataset (N=22,661) were used to predict brain-age in 2,314 cognitively unimpaired (CU) individuals at higher risk of AD and mild cognitive impaired (MCI) patients from four independent cohorts with available biomarker data: ALFA+, ADNI, EPAD, and OASIS. Brain-age delta was associated with abnormal amyloid-β, more advanced stages (AT) of AD pathology and APOE-ε4 status. Brain-age delta was positively associated with plasma neurofilament light, a marker of neurodegeneration, and sex differences in the brain effects of this marker were found. These results validate brain-age delta as a non-invasive marker of biological brain aging in non-demented individuals with abnormal levels of biomarkers of AD and axonal injury.},
	urldate = {2025-07-06},
	journal = {eLife},
	author = {Cumplido-Mayoral, Irene and García-Prat, Marina and Operto, Grégory and Falcon, Carles and Shekari, Mahnaz and Cacciaglia, Raffaele and Milà-Alomà, Marta and Lorenzini, Luigi and Ingala, Silvia and Meije Wink, Alle and Mutsaerts, Henk JMM and Minguillón, Carolina and Fauria, Karine and Molinuevo, José Luis and Haller, Sven and Chetelat, Gael and Waldman, Adam and Schwarz, Adam J and Barkhof, Frederik and Suridjan, Ivonne and Kollmorgen, Gwendlyn and Bayfield, Anna and Zetterberg, Henrik and Blennow, Kaj and Suárez-Calvet, Marc and Vilaplana, Verónica and Gispert, Juan Domingo and {ALFA study} and {EPAD study} and {ADNI study} and {OASIS study}},
	editor = {Miller, Karla L and Wong, Ma-Li and Boyle, Rory and Cole, James},
	month = apr,
	year = {2023},
	note = {Publisher: eLife Sciences Publications, Ltd},
	keywords = {alzheimer's disease, brain age prediction, neuroimaging},
	pages = {e81067},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/Q85RTC48/Cumplido-Mayoral et al. - 2023 - Biological brain age prediction using machine learning on structural neuroimaging data Multi-cohort.pdf:application/pdf},
}

@article{ghosh_surface-based_2022,
	title = {Surface-based brain morphometry in schizophrenia vs. cannabis-induced psychosis: {A} controlled comparison},
	volume = {155},
	issn = {0022-3956},
	url = {https://www.sciencedirect.com/science/article/pii/S0022395622005258},
	doi = {https://doi.org/10.1016/j.jpsychires.2022.09.034},
	abstract = {Background \& aim We examined group differences in cortical thickness and surface-parameters among age and handedness–matched persons with cannabis-induced psychosis (CIP), schizophrenia with heavy cannabis use (SZC), and healthy controls (HC). Methods We recruited 31 men with SZC, 28 with CIP, and 30 with HC. We used the Psychiatric Research Interview for Substance and Mental Disorders to differentiate between CIP and SZC. We processed and analyzed T1 MR images using the Surface-based Brain Morphometry (SBM) pipeline of the CAT-12 toolbox within the statistical parametric mapping. After pre-processing, volumes were segmented using surface and thickness estimation for the analysis of the region of interest. We used the projection-based thickness method to assess the cortical thickness and Desikan-Killiany atlas for cortical parcellation. Results We observed the lowest cortical thickness, depth, and gyrification in the SZC, followed by CIP and the control groups. The differences were predominantly seen in frontal cortices, with limited parietal and temporal regions involvement. After False Discovery Rate (FDR) corrections and post-hoc analysis, SZC had reduced cortical thickness than HC in the middle and inferior frontal, right entorhinal, and left postcentral regions. Cortical thickness of SZC was also significantly lower than CIP in bilateral postcentral and right middle frontal regions. We found negative correlations (after FDR corrections) between the duration of cannabis use and cortical thickness in loci of parietal and occipital cortices. Conclusion Our study suggested cortical structural abnormalities in schizophrenia, in reference to healthy controls and cannabis-induced psychosis, indicating different pathophysiology of SZC and CIP.},
	journal = {Journal of Psychiatric Research},
	author = {Ghosh, Abhishek and Kaur, Simranjit and Shah, Raghav and Oomer, Fareed and Avasthi, Ajit and Ahuja, Chirag K. and Basu, Debasish and Nehra, Ritu and Khandelwal, Niranjan},
	year = {2022},
	pages = {286--294},
}


@misc{silva_biomechanical_2020,
	title = {Biomechanical modelling of brain atrophy through deep learning},
	url = {http://arxiv.org/abs/2012.07596},
	doi = {10.48550/arXiv.2012.07596},
	abstract = {We present a proof-of-concept, deep learning (DL) based, differentiable biomechanical model of realistic brain deformations. Using prescribed maps of local atrophy and growth as input, the network learns to deform images according to a Neo-Hookean model of tissue deformation. The tool is validated using longitudinal brain atrophy data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and we demonstrate that the trained model is capable of rapidly simulating new brain deformations with minimal residuals. This method has the potential to be used in data augmentation or for the exploration of different causal hypotheses reflecting brain growth and atrophy.},
	urldate = {2025-07-07},
	publisher = {arXiv},
	author = {Silva, Mariana da and Garcia, Kara and Sudre, Carole H. and Bass, Cher and Cardoso, M. Jorge and Robinson, Emma},
	month = dec,
	year = {2020},
	note = {arXiv:2012.07596 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Tissues and Organs, Statistics - Machine Learning},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/BAR7WGN9/Silva et al. - 2020 - Biomechanical modelling of brain atrophy through deep learning.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/M85C55JK/2012.html:text/html},
}

@misc{silva_distinguishing_2021,
	title = {Distinguishing {Healthy} {Ageing} from {Dementia}: a {Biomechanical} {Simulation} of {Brain} {Atrophy} using {Deep} {Networks}},
	shorttitle = {Distinguishing {Healthy} {Ageing} from {Dementia}},
	url = {http://arxiv.org/abs/2108.08214},
	doi = {10.48550/arXiv.2108.08214},
	abstract = {Biomechanical modeling of tissue deformation can be used to simulate different scenarios of longitudinal brain evolution. In this work,we present a deep learning framework for hyper-elastic strain modelling of brain atrophy, during healthy ageing and in Alzheimer's Disease. The framework directly models the effects of age, disease status, and scan interval to regress regional patterns of atrophy, from which a strain-based model estimates deformations. This model is trained and validated using 3D structural magnetic resonance imaging data from the ADNI cohort. Results show that the framework can estimate realistic deformations, following the known course of Alzheimer's disease, that clearly differentiate between healthy and demented patterns of ageing. This suggests the framework has potential to be incorporated into explainable models of disease, for the exploration of interventions and counterfactual examples.},
	urldate = {2025-07-07},
	publisher = {arXiv},
	author = {Silva, Mariana Da and Sudre, Carole H. and Garcia, Kara and Bass, Cher and Cardoso, M. Jorge and Robinson, Emma C.},
	month = aug,
	year = {2021},
	note = {arXiv:2108.08214 [q-bio]},
	keywords = {Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Neurons and Cognition, Quantitative Biology - Tissues and Organs},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/VQBHJGX8/Silva et al. - 2021 - Distinguishing Healthy Ageing from Dementia a Biomechanical Simulation of Brain Atrophy using Deep.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/DGA5S5CV/2108.html:text/html},
}



@misc{sang_4deform_2025,
	title = {{4Deform}: {Neural} {Surface} {Deformation} for {Robust} {Shape} {Interpolation}},
	shorttitle = {{4Deform}},
	url = {http://arxiv.org/abs/2502.20208},
	doi = {10.48550/arXiv.2502.20208},
	abstract = {Generating realistic intermediate shapes between non-rigidly deformed shapes is a challenging task in computer vision, especially with unstructured data (e.g., point clouds) where temporal consistency across frames is lacking, and topologies are changing. Most interpolation methods are designed for structured data (i.e., meshes) and do not apply to real-world point clouds. In contrast, our approach, 4Deform, leverages neural implicit representation (NIR) to enable free topology changing shape deformation. Unlike previous mesh-based methods that learn vertex-based deformation fields, our method learns a continuous velocity field in Euclidean space. Thus, it is suitable for less structured data such as point clouds. Additionally, our method does not require intermediate-shape supervision during training; instead, we incorporate physical and geometrical constraints to regularize the velocity field. We reconstruct intermediate surfaces using a modified level-set equation, directly linking our NIR with the velocity field. Experiments show that our method significantly outperforms previous NIR approaches across various scenarios (e.g., noisy, partial, topology-changing, non-isometric shapes) and, for the first time, enables new applications like 4D Kinect sequence upsampling and real-world high-resolution mesh deformation.},
	urldate = {2025-07-07},
	publisher = {arXiv},
	author = {Sang, Lu and Canfes, Zehranaz and Cao, Dongliang and Marin, Riccardo and Bernard, Florian and Cremers, Daniel},
	month = feb,
	year = {2025},
	note = {arXiv:2502.20208 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/7GP7ETVR/Sang et al. - 2025 - 4Deform Neural Surface Deformation for Robust Shape Interpolation.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/PI6LCHF4/2502.html:text/html},
}

@article{botsch_primo_nodate,
	title = {{PriMo}: {Coupled} {Prisms} for {Intuitive} {Surface} {Modeling}},
	abstract = {We present a new method for 3D shape modeling that achieves intuitive and robust deformations by emulating physically plausible surface behavior inspired by thin shells and plates. The surface mesh is embedded in a layer of volumetric prisms, which are coupled through non-linear, elastic forces. To deform the mesh, prisms are rigidly transformed to satisfy user constraints while minimizing the elastic energy. The rigidity of the prisms prevents degenerations even under extreme deformations, making the method numerically stable. For the underlying geometric optimization we employ both local and global shape matching techniques. Our modeling framework allows for the speciﬁcation of various geometrically intuitive parameters that provide control over the physical surface behavior. While computationally more involved than previous methods, our approach signiﬁcantly improves robustness and simpliﬁes user interaction for large, complex deformations.},
	language = {en},
	author = {Botsch, Mario and Pauly, Mark and Gross, Markus and Kobbelt, Leif},
	file = {PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/2H76AWVU/Botsch et al. - PriMo Coupled Prisms for Intuitive Surface Modeling.pdf:application/pdf},
}

@misc{oehri_higher_2025,
	title = {Higher {Order} {Continuity} for {Smooth} {As}-{Rigid}-{As}-{Possible} {Shape} {Modeling}},
	url = {http://arxiv.org/abs/2501.10335},
	doi = {10.48550/arXiv.2501.10335},
	abstract = {We propose a modification of the As-Rigid-As-Possible (ARAP) mesh deformation energy with higher order smoothness, which overcomes a prominent limitation of the original ARAP formulation: spikes and lack of continuity at the manipulation handles. Our method avoids spikes even when using single-point positional constraints. Since no explicit rotations have to be specified, the user interaction can be realized through a simple click-and-drag interface, where points on the mesh can be selected and moved around while the rest of the mesh surface automatically deforms accordingly. Our method preserves the benefits of ARAP deformations: it is easy to implement and thus useful for practical applications, while its efficiency makes it usable in real-time, interactive scenarios on detailed models.},
	urldate = {2025-07-07},
	publisher = {arXiv},
	author = {Oehri, Annika and Herholz, Philipp and Sorkine-Hornung, Olga},
	month = jan,
	year = {2025},
	note = {arXiv:2501.10335 [cs]},
	keywords = {Computer Science - Computational Geometry},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/E4VM2QMC/Oehri et al. - 2025 - Higher Order Continuity for Smooth As-Rigid-As-Possible Shape Modeling.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/6446RNA4/2501.html:text/html},
}

@article{sorkine_as-rigid-as-possible_nodate,
	title = {As-{Rigid}-{As}-{Possible} {Surface} {Modeling}},
	abstract = {Modeling tasks, such as surface deformation and editing, can be analyzed by observing the local behavior of the surface. We argue that deﬁning a modeling operation by asking for rigidity of the local transformations is useful in various settings. Such formulation leads to a non-linear, yet conceptually simple energy formulation, which is to be minimized by the deformed surface under particular modeling constraints. We devise a simple iterative mesh editing scheme based on this principle, that leads to detail-preserving and intuitive deformations. Our algorithm is effective and notably easy to implement, making it attractive for practical modeling applications.},
	language = {en},
	author = {Sorkine, Olga and Alexa, Marc},
	file = {PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/KKLGBKKV/Sorkine and Alexa - As-Rigid-As-Possible Surface Modeling.pdf:application/pdf},
}

@misc{bastian_hybrid_2024,
	title = {Hybrid {Functional} {Maps} for {Crease}-{Aware} {Non}-{Isometric} {Shape} {Matching}},
	url = {http://arxiv.org/abs/2312.03678},
	doi = {10.48550/arXiv.2312.03678},
	abstract = {Non-isometric shape correspondence remains a fundamental challenge in computer vision. Traditional methods using Laplace-Beltrami operator (LBO) eigenmodes face limitations in characterizing high-frequency extrinsic shape changes like bending and creases. We propose a novel approach of combining the non-orthogonal extrinsic basis of eigenfunctions of the elastic thin-shell hessian with the intrinsic ones of the LBO, creating a hybrid spectral space in which we construct functional maps. To this end, we present a theoretical framework to effectively integrate non-orthogonal basis functions into descriptor- and learning-based functional map methods. Our approach can be incorporated easily into existing functional map pipelines across varying applications and is able to handle complex deformations beyond isometries. We show extensive evaluations across various supervised and unsupervised settings and demonstrate significant improvements. Notably, our approach achieves up to 15\% better mean geodesic error for non-isometric correspondence settings and up to 45\% improvement in scenarios with topological noise.},
	urldate = {2025-07-07},
	publisher = {arXiv},
	author = {Bastian, Lennart and Xie, Yizheng and Navab, Nassir and Lähner, Zorah},
	month = jul,
	year = {2024},
	note = {arXiv:2312.03678 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/BXBDHCC3/Bastian et al. - 2024 - Hybrid Functional Maps for Crease-Aware Non-Isometric Shape Matching.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/PUEJ96ET/2312.html:text/html},
}



@inproceedings{ghahremani_h-vit_2024,
	address = {Seattle, WA, USA},
	title = {H-{ViT}: {A} {Hierarchical} {Vision} {Transformer} for {Deformable} {Image} {Registration}},
	copyright = {https://doi.org/10.15223/policy-029},
	isbn = {979-8-3503-5300-6},
	shorttitle = {H-{ViT}},
	url = {https://ieeexplore.ieee.org/document/10656382/},
	doi = {10.1109/CVPR52733.2024.01094},
	abstract = {This paper introduces a novel top-down representation approach for deformable image registration, which estimates the deformation field by capturing various shortand long-range flow features at different scale levels. As a Hierarchical Vision Transformer (H-ViT), we propose a dual self-attention and cross-attention mechanism that uses high-level features in the deformation field to represent lowlevel ones, enabling information streams in the deformation field across all voxel patch embeddings irrespective of their spatial proximity. Since high-level features contain abstract flow patterns, such patterns are expected to effectively contribute to the representation of the deformation field in lower scales. When the self-attention module utilizes within-scale short-range patterns for representation, the cross-attention modules dynamically look for the key tokens across different scales to further interact with the local query voxel patches. Our method shows superior accuracy and visual quality over the state-of-the-art registration methods in five publicly available datasets, highlighting a substantial enhancement in the performance of medical imaging registration. The project link is available at https://mogvision.github.io/hvit.},
	language = {en},
	urldate = {2025-02-05},
	booktitle = {2024 {IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})},
	publisher = {IEEE},
	author = {Ghahremani, Morteza and Khateri, Mohammad and Jian, Bailiang and Wiestler, Benedikt and Adeli, Ehsan and Wachinger, Christian},
	month = jun,
	year = {2024},
	pages = {11513--11523},
	file = {PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/2ANTD957/Ghahremani et al. - 2024 - H-ViT A Hierarchical Vision Transformer for Deformable Image Registration.pdf:application/pdf},
}

@inproceedings{cheng_unsupervised_2025,
	title = {Unsupervised {Cortical} {Surface} {Registration} {Network} for {Aligning} {Gyralnet}},
	url = {https://ieeexplore.ieee.org/document/10981138/},
	doi = {10.1109/ISBI60581.2025.10981138},
	abstract = {The cortical 3-hinge gyrus (3HG) and its network (GyralNet) play key roles in understanding the regularity and variability of brain structure and function. However, existing cortical surface registration methods overlook these features, resulting in suboptimal alignment across subjects. Currently, no 3HG and GyralNet atlas exist for registration, and generation of the corresponding atlas requires extensive runtime using traditional methods. To enable better registration of these features, we introduce an unsupervised learning framework to jointly develop 3HGs and GyralNet atlas and register the individual cortical features onto the atlas. To incorporate the graph structure of 3HGs and GyralNet into the registration network, we convert them into surface distance maps, facilitating effective integration. To effectively learn large deformations, a multi-level spherical registration network based on spherical U-Net is introduced to perform registration in a coarse-to-fine manner. Experiments demonstrate our approach's ability to generate 3HGs and GyralNet atlas with detailed patterns and effectively improve registration accuracy.},
	urldate = {2025-05-28},
	booktitle = {2025 {IEEE} 22nd {International} {Symposium} on {Biomedical} {Imaging} ({ISBI})},
	author = {Cheng, Jiale and Zhao, Fenqiang and Hu, Dan and Cao, Chao and Wu, Zhengwang and Yuan, Xinrui and Han, Kangfu and Zhang, Lu and Liu, Tianming and Zhu, Dajiang and Li, Gang},
	month = apr,
	year = {2025},
	note = {ISSN: 1945-8452},
	keywords = {Brain, Deformation, Biomedical imaging, 3-Hinge Gyrus, Accuracy, Cortical Surface Registration, GyralNet, Registers, Runtime, Spherical U-net, Unsupervised learning},
	pages = {01--05},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/JE677HTD/Cheng et al. - 2025 - Unsupervised Cortical Surface Registration Network for Aligning Gyralnet.pdf:application/pdf},
}

@article{nenning_diffeomorphic_2017,
	title = {Diffeomorphic functional brain surface alignment: {Functional} demons},
	volume = {156},
	issn = {1053-8119},
	shorttitle = {Diffeomorphic functional brain surface alignment},
	url = {https://www.sciencedirect.com/science/article/pii/S105381191730321X},
	doi = {10.1016/j.neuroimage.2017.04.028},
	abstract = {Aligning brain structures across individuals is a central prerequisite for comparative neuroimaging studies. Typically, registration approaches assume a strong association between the features used for alignment, such as macro-anatomy, and the variable observed, such as functional activation or connectivity. Here, we propose to use the structure of intrinsic resting state fMRI signal correlation patterns as a basis for alignment of the cortex in functional studies. Rather than assuming the spatial correspondence of functional structures between subjects, we have identified locations with similar connectivity profiles across subjects. We mapped functional connectivity relationships within the brain into an embedding space, and aligned the resulting maps of multiple subjects. We then performed a diffeomorphic alignment of the cortical surfaces, driven by the corresponding features in the joint embedding space. Results show that functional alignment based on resting state fMRI identifies functionally homologous regions across individuals with higher accuracy than alignment based on the spatial correspondence of anatomy. Further, functional alignment enables measurement of the strength of the anatomo-functional link across the cortex, and reveals the uneven distribution of this link. Stronger anatomo-functional dissociation was found in higher association areas compared to primary sensory- and motor areas. Functional alignment based on resting state features improves group analysis of task based functional MRI data, increasing statistical power and improving the delineation of task-specific core regions. Finally, a comparison of the anatomo-functional dissociation between cohorts is demonstrated with a group of left and right handed subjects.},
	urldate = {2025-07-09},
	journal = {NeuroImage},
	author = {Nenning, Karl-Heinz and Liu, Hesheng and Ghosh, Satrajit S. and Sabuncu, Mert R. and Schwartz, Ernst and Langs, Georg},
	month = aug,
	year = {2017},
	pages = {456--465},
	file = {Accepted Version:/home/localaak22/snap/zotero-snap/common/Zotero/storage/ZHXSV3WR/Nenning et al. - 2017 - Diffeomorphic functional brain surface alignment Functional demons.pdf:application/pdf;ScienceDirect Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/KPA6F62U/S105381191730321X.html:text/html},
}

@article{avants_symmetric_2008,
	series = {Special {Issue} on {The} {Third} {International} {Workshop} on {Biomedical} {Image} {Registration} – {WBIR} 2006},
	title = {Symmetric diffeomorphic image registration with cross-correlation: {Evaluating} automated labeling of elderly and neurodegenerative brain},
	volume = {12},
	issn = {1361-8415},
	shorttitle = {Symmetric diffeomorphic image registration with cross-correlation},
	url = {https://www.sciencedirect.com/science/article/pii/S1361841507000606},
	doi = {10.1016/j.media.2007.06.004},
	abstract = {One of the most challenging problems in modern neuroimaging is detailed characterization of neurodegeneration. Quantifying spatial and longitudinal atrophy patterns is an important component of this process. These spatiotemporal signals will aid in discriminating between related diseases, such as frontotemporal dementia (FTD) and Alzheimer’s disease (AD), which manifest themselves in the same at-risk population. Here, we develop a novel symmetric image normalization method (SyN) for maximizing the cross-correlation within the space of diffeomorphic maps and provide the Euler–Lagrange equations necessary for this optimization. We then turn to a careful evaluation of our method. Our evaluation uses gold standard, human cortical segmentation to contrast SyN’s performance with a related elastic method and with the standard ITK implementation of Thirion’s Demons algorithm. The new method compares favorably with both approaches, in particular when the distance between the template brain and the target brain is large. We then report the correlation of volumes gained by algorithmic cortical labelings of FTD and control subjects with those gained by the manual rater. This comparison shows that, of the three methods tested, SyN’s volume measurements are the most strongly correlated with volume measurements gained by expert labeling. This study indicates that SyN, with cross-correlation, is a reliable method for normalizing and making anatomical measurements in volumetric MRI of patients and at-risk elderly individuals.},
	number = {1},
	urldate = {2025-07-09},
	journal = {Medical Image Analysis},
	author = {Avants, B. B. and Epstein, C. L. and Grossman, M. and Gee, J. C.},
	month = feb,
	year = {2008},
	keywords = {Cross-correlation, Deformable image registration, Dementia, Diffeomorphic, Human cortex, Morphometry},
	pages = {26--41},
	file = {Accepted Version:/home/localaak22/snap/zotero-snap/common/Zotero/storage/Y25TFW9G/Avants et al. - 2008 - Symmetric diffeomorphic image registration with cross-correlation Evaluating automated labeling of.pdf:application/pdf;ScienceDirect Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/G3JB45E4/S1361841507000606.html:text/html},
}

@article{beg_computing_2005,
	title = {Computing {Large} {Deformation} {Metric} {Mappings} via {Geodesic} {Flows} of {Diffeomorphisms}},
	volume = {61},
	issn = {1573-1405},
	url = {https://doi.org/10.1023/B:VISI.0000043755.93987.aa},
	doi = {10.1023/B:VISI.0000043755.93987.aa},
	abstract = {This paper examine the Euler-Lagrange equations for the solution of the large deformation diffeomorphic metric mapping problem studied in Dupuis et al. (1998) and Trouvé (1995) in which two images I								0, I								1 are given and connected via the diffeomorphic change of coordinates I								0○ϕ−1=I								1 where ϕ=Φ1 is the end point at t= 1 of curve Φt, t∈[0, 1] satisfying .Φt=v								t (Φt), t∈ [0,1] with Φ0=id. The variational problem takes the form},
	language = {en},
	number = {2},
	urldate = {2025-07-09},
	journal = {Int J Comput Vision},
	author = {Beg, M. Faisal and Miller, Michael I. and Trouvé, Alain and Younes, Laurent},
	month = feb,
	year = {2005},
	keywords = {Calculus of Variations and Optimization, Computational Anatomy, Deformable Template, Differential Geometry, Diffusion  Processes and Stochastic Analysis on  Manifolds, Euler-Lagrange Equation, Global Analysis and Analysis on Manifolds, Hyperbolic Geometry, Metrics, Partial Differential Equations on Manifolds, Variational Optimization},
	pages = {139--157},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/L7QBBQXC/Beg et al. - 2005 - Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms.pdf:application/pdf},
}

@article{krebs_learning_2019,
	title = {Learning a {Probabilistic} {Model} for {Diffeomorphic} {Registration}},
	volume = {38},
	issn = {0278-0062, 1558-254X},
	url = {http://arxiv.org/abs/1812.07460},
	doi = {10.1109/TMI.2019.2897112},
	abstract = {We propose to learn a low-dimensional probabilistic deformation model from data which can be used for registration and the analysis of deformations. The latent variable model maps similar deformations close to each other in an encoding space. It enables to compare deformations, generate normal or pathological deformations for any new image or to transport deformations from one image pair to any other image. Our unsupervised method is based on variational inference. In particular, we use a conditional variational autoencoder (CVAE) network and constrain transformations to be symmetric and diffeomorphic by applying a differentiable exponentiation layer with a symmetric loss function. We also present a formulation that includes spatial regularization such as diffusion-based filters. Additionally, our framework provides multi-scale velocity field estimations. We evaluated our method on 3-D intra-subject registration using 334 cardiac cine-MRIs. On this dataset, our method showed state-of-the-art performance with a mean DICE score of 81.2\% and a mean Hausdorff distance of 7.3mm using 32 latent dimensions compared to three state-of-the-art methods while also demonstrating more regular deformation fields. The average time per registration was 0.32s. Besides, we visualized the learned latent space and show that the encoded deformations can be used to transport deformations and to cluster diseases with a classification accuracy of 83\% after applying a linear projection.},
	number = {9},
	urldate = {2025-07-09},
	journal = {IEEE Trans. Med. Imaging},
	author = {Krebs, Julian and Delingette, Hervé and Mailhé, Boris and Ayache, Nicholas and Mansi, Tommaso},
	month = sep,
	year = {2019},
	note = {arXiv:1812.07460 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	pages = {2165--2176},
	file = {Preprint PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/CCLSKGLJ/Krebs et al. - 2019 - Learning a Probabilistic Model for Diffeomorphic Registration.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/TAH3XDMI/1812.html:text/html},
}

@misc{sokooti_3d_2019,
	title = {{3D} {Convolutional} {Neural} {Networks} {Image} {Registration} {Based} on {Efficient} {Supervised} {Learning} from {Artificial} {Deformations}},
	url = {http://arxiv.org/abs/1908.10235},
	doi = {10.48550/arXiv.1908.10235},
	abstract = {We propose a supervised nonrigid image registration method, trained using artificial displacement vector fields (DVF), for which we propose and compare three network architectures. The artificial DVFs allow training in a fully supervised and voxel-wise dense manner, but without the cost usually associated with the creation of densely labeled data. We propose a scheme to artificially generate DVFs, and for chest CT registration augment these with simulated respiratory motion. The proposed architectures are embedded in a multi-stage approach, to increase the capture range of the proposed networks in order to more accurately predict larger displacements. The proposed method, RegNet, is evaluated on multiple databases of chest CT scans and achieved a target registration error of 2.32 \${\textbackslash}pm\$ 5.33 mm and 1.86 \${\textbackslash}pm\$ 2.12 mm on SPREAD and DIR-Lab-4DCT studies, respectively. The average inference time of RegNet with two stages is about 2.2 s.},
	urldate = {2025-07-09},
	publisher = {arXiv},
	author = {Sokooti, Hessam and Vos, Bob de and Berendsen, Floris and Ghafoorian, Mohsen and Yousefi, Sahar and Lelieveldt, Boudewijn P. F. and Isgum, Ivana and Staring, Marius},
	month = aug,
	year = {2019},
	note = {arXiv:1908.10235 [eess]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing},
	file = {Preprint PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/BUEYHPMZ/Sokooti et al. - 2019 - 3D Convolutional Neural Networks Image Registration Based on Efficient Supervised Learning from Arti.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/73KTZ3QY/1908.html:text/html},
}

@misc{yang_fast_2016,
	title = {Fast {Predictive} {Image} {Registration}},
	url = {http://arxiv.org/abs/1607.02504},
	doi = {10.48550/arXiv.1607.02504},
	abstract = {We present a method to predict image deformations based on patch-wise image appearance. Specifically, we design a patch-based deep encoder-decoder network which learns the pixel/voxel-wise mapping between image appearance and registration parameters. Our approach can predict general deformation parameterizations, however, we focus on the large deformation diffeomorphic metric mapping (LDDMM) registration model. By predicting the LDDMM momentum-parameterization we retain the desirable theoretical properties of LDDMM, while reducing computation time by orders of magnitude: combined with patch pruning, we achieve a 1500x/66x speed up compared to GPU-based optimization for 2D/3D image registration. Our approach has better prediction accuracy than predicting deformation or velocity fields and results in diffeomorphic transformations. Additionally, we create a Bayesian probabilistic version of our network, which allows evaluation of deformation field uncertainty through Monte Carlo sampling using dropout at test time. We show that deformation uncertainty highlights areas of ambiguous deformations. We test our method on the OASIS brain image dataset in 2D and 3D.},
	urldate = {2025-07-09},
	publisher = {arXiv},
	author = {Yang, Xiao and Kwitt, Roland and Niethammer, Marc},
	month = jul,
	year = {2016},
	note = {arXiv:1607.02504 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	file = {Preprint PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/GNM3788P/Yang et al. - 2016 - Fast Predictive Image Registration.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/F36DIC5M/1607.html:text/html},
}

@article{cao_deformable_2017,
	title = {Deformable {Image} {Registration} based on {Similarity}-{Steered} {CNN} {Regression}},
	volume = {10433},
	doi = {10.1007/978-3-319-66182-7_35},
	abstract = {Existing deformable registration methods require exhaustively iterative optimization, along with careful parameter tuning, to estimate the deformation field between images. Although some learning-based methods have been proposed for initiating deformation estimation, they are often template-specific and not flexible in practical use. In this paper, we propose a convolutional neural network (CNN) based regression model to directly learn the complex mapping from the input image pair (i.e., a pair of template and subject) to their corresponding deformation field. Specifically, our CNN architecture is designed in a patch-based manner to learn the complex mapping from the input patch pairs to their respective deformation field. First, the equalized active-points guided sampling strategy is introduced to facilitate accurate CNN model learning upon a limited image dataset. Then, the similarity-steered CNN architecture is designed, where we propose to add the auxiliary contextual cue, i.e., the similarity between input patches, to more directly guide the learning process. Experiments on different brain image datasets demonstrate promising registration performance based on our CNN model. Furthermore, it is found that the trained CNN model from one dataset can be successfully transferred to another dataset, although brain appearances across datasets are quite variable.},
	language = {eng},
	journal = {Med Image Comput Comput Assist Interv},
	author = {Cao, Xiaohuan and Yang, Jianhua and Zhang, Jun and Nie, Dong and Kim, Min-Jeong and Wang, Qian and Shen, Dinggang},
	month = sep,
	year = {2017},
	pmid = {29250613},
	pmcid = {PMC5731783},
	keywords = {Algorithms, Brain, Humans, Machine Learning, Neural Networks, Computer, Neuroimaging, Reproducibility of Results, Sensitivity and Specificity, Young Adult},
	pages = {300--308},
}

@inproceedings{dalca_anatomical_2018,
	title = {Anatomical {Priors} in {Convolutional} {Networks} for {Unsupervised} {Biomedical} {Segmentation}},
	url = {http://arxiv.org/abs/1903.03148},
	doi = {10.1109/CVPR.2018.00968},
	abstract = {We consider the problem of segmenting a biomedical image into anatomical regions of interest. We specifically address the frequent scenario where we have no paired training data that contains images and their manual segmentations. Instead, we employ unpaired segmentation images to build an anatomical prior. Critically these segmentations can be derived from imaging data from a different dataset and imaging modality than the current task. We introduce a generative probabilistic model that employs the learned prior through a convolutional neural network to compute segmentations in an unsupervised setting. We conducted an empirical analysis of the proposed approach in the context of structural brain MRI segmentation, using a multi-study dataset of more than 14,000 scans. Our results show that an anatomical prior can enable fast unsupervised segmentation which is typically not possible using standard convolutional networks. The integration of anatomical priors can facilitate CNN-based anatomical segmentation in a range of novel clinical problems, where few or no annotations are available and thus standard networks are not trainable. The code is freely available at http://github.com/adalca/neuron.},
	urldate = {2025-07-09},
	booktitle = {2018 {IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition}},
	author = {Dalca, Adrian V. and Guttag, John and Sabuncu, Mert R.},
	month = jun,
	year = {2018},
	note = {arXiv:1903.03148 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	pages = {9290--9299},
	file = {Preprint PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/HXPG8DP9/Dalca et al. - 2018 - Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/42SXPPJP/1903.html:text/html},
}

@article{cheng_cortical_2020,
	title = {Cortical surface registration using unsupervised learning},
	volume = {221},
	issn = {1053-8119},
	url = {https://www.sciencedirect.com/science/article/pii/S1053811920306479},
	doi = {10.1016/j.neuroimage.2020.117161},
	abstract = {Non-rigid cortical registration is an important and challenging task due to the geometric complexity of the human cortex and the high degree of inter-subject variability. A conventional solution is to use a spherical representation of surface properties and perform registration by aligning cortical folding patterns in that space. This strategy produces accurate spatial alignment, but often requires high computational cost. Recently, convolutional neural networks (CNNs) have demonstrated the potential to dramatically speed up volumetric registration. However, due to distortions introduced by projecting a sphere to a 2D plane, a direct application of recent learning-based methods to surfaces yields poor results. In this study, we present SphereMorph, a diffeomorphic registration framework for cortical surfaces using deep networks that addresses these issues. SphereMorph uses a UNet-style network associated with a spherical kernel to learn the displacement field and warps the sphere using a modified spatial transformer layer. We propose a resampling weight in computing the data fitting loss to account for distortions introduced by polar projection, and demonstrate the performance of our proposed method on two tasks, including cortical parcellation and group-wise functional area alignment. The experiments show that the proposed SphereMorph is capable of modeling the geometric registration problem in a CNN framework and demonstrate superior registration accuracy and computational efficiency. The source code of SphereMorph will be released to the public upon acceptance of this manuscript at https://github.com/voxelmorph/spheremorph.},
	urldate = {2025-07-09},
	journal = {NeuroImage},
	author = {Cheng, Jieyu and Dalca, Adrian V. and Fischl, Bruce and Zöllei, Lilla},
	month = nov,
	year = {2020},
	keywords = {Cortical surface registration, Deep learning, SphereMorph, Subject-to-atlas registration, Unsupervised learning},
	pages = {117161},
	file = {Full Text:/home/localaak22/snap/zotero-snap/common/Zotero/storage/IM4TMXCL/Cheng et al. - 2020 - Cortical surface registration using unsupervised learning.pdf:application/pdf;ScienceDirect Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/NW2MTBN2/S1053811920306479.html:text/html},
}

@article{cheng_cortical_2020-1,
	title = {Cortical surface registration using unsupervised learning},
	volume = {221},
	issn = {10538119},
	url = {http://arxiv.org/abs/2004.04617},
	doi = {10.1016/j.neuroimage.2020.117161},
	abstract = {Non-rigid cortical registration is an important and challenging task due to the geometric complexity of the human cortex and the high degree of inter-subject variability. A conventional solution is to use a spherical representation of surface properties and perform registration by aligning cortical folding patterns in that space. This strategy produces accurate spatial alignment but often requires a high computational cost. Recently, convolutional neural networks (CNNs) have demonstrated the potential to dramatically speed up volumetric registration. However, due to distortions introduced by projecting a sphere to a 2D plane, a direct application of recent learning-based methods to surfaces yields poor results. In this study, we present SphereMorph, a diffeomorphic registration framework for cortical surfaces using deep networks that addresses these issues. SphereMorph uses a UNet-style network associated with a spherical kernel to learn the displacement field and warps the sphere using a modified spatial transformer layer. We propose a resampling weight in computing the data fitting loss to account for distortions introduced by polar projection, and demonstrate the performance of our proposed method on two tasks, including cortical parcellation and group-wise functional area alignment. The experiments show that the proposed SphereMorph is capable of modeling the geometric registration problem in a CNN framework and demonstrate superior registration accuracy and computational efficiency. The source code of SphereMorph will be released to the public upon acceptance of this manuscript at https://github.com/voxelmorph/spheremorph.},
	urldate = {2025-07-09},
	journal = {NeuroImage},
	author = {Cheng, Jieyu and Dalca, Adrian V. and Fischl, Bruce and Zollei, Lilla},
	month = nov,
	year = {2020},
	note = {arXiv:2004.04617 [eess]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Neurons and Cognition},
	pages = {117161},
	file = {Full Text PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/HBVF5DQH/Cheng et al. - 2020 - Cortical surface registration using unsupervised learning.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/NSTJBNKY/2004.html:text/html},
}

@article{vos_deep_2019,
	title = {A {Deep} {Learning} {Framework} for {Unsupervised} {Affine} and {Deformable} {Image} {Registration}},
	volume = {52},
	issn = {13618415},
	url = {http://arxiv.org/abs/1809.06130},
	doi = {10.1016/j.media.2018.11.010},
	abstract = {Image registration, the process of aligning two or more images, is the core technique of many (semi-)automatic medical image analysis tasks. Recent studies have shown that deep learning methods, notably convolutional neural networks (ConvNets), can be used for image registration. Thus far training of ConvNets for registration was supervised using predefined example registrations. However, obtaining example registrations is not trivial. To circumvent the need for predefined examples, and thereby to increase convenience of training ConvNets for image registration, we propose the Deep Learning Image Registration (DLIR) framework for {\textbackslash}textit\{unsupervised\} affine and deformable image registration. In the DLIR framework ConvNets are trained for image registration by exploiting image similarity analogous to conventional intensity-based image registration. After a ConvNet has been trained with the DLIR framework, it can be used to register pairs of unseen images in one shot. We propose flexible ConvNets designs for affine image registration and for deformable image registration. By stacking multiple of these ConvNets into a larger architecture, we are able to perform coarse-to-fine image registration. We show for registration of cardiac cine MRI and registration of chest CT that performance of the DLIR framework is comparable to conventional image registration while being several orders of magnitude faster.},
	urldate = {2025-07-09},
	journal = {Medical Image Analysis},
	author = {Vos, Bob D. de and Berendsen, Floris F. and Viergever, Max A. and Sokooti, Hessam and Staring, Marius and Isgum, Ivana},
	month = feb,
	year = {2019},
	note = {arXiv:1809.06130 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	pages = {128--143},
	file = {Preprint PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/NF4MLMNX/Vos et al. - 2019 - A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/N8PNIPU4/1809.html:text/html},
}

@misc{li_joint_2023,
	title = {Joint cortical registration of geometry and function using semi-supervised learning},
	url = {http://arxiv.org/abs/2303.01592},
	doi = {10.48550/arXiv.2303.01592},
	abstract = {Brain surface-based image registration, an important component of brain image analysis, establishes spatial correspondence between cortical surfaces. Existing iterative and learning-based approaches focus on accurate registration of folding patterns of the cerebral cortex, and assume that geometry predicts function and thus functional areas will also be well aligned. However, structure/functional variability of anatomically corresponding areas across subjects has been widely reported. In this work, we introduce a learning-based cortical registration framework, JOSA, which jointly aligns folding patterns and functional maps while simultaneously learning an optimal atlas. We demonstrate that JOSA can substantially improve registration performance in both anatomical and functional domains over existing methods. By employing a semi-supervised training strategy, the proposed framework obviates the need for functional data during inference, enabling its use in broad neuroscientific domains where functional data may not be observed. The source code of JOSA will be released to the public at https://voxelmorph.net.},
	urldate = {2025-07-09},
	publisher = {arXiv},
	author = {Li, Jian and Tuckute, Greta and Fedorenko, Evelina and Edlow, Brian L. and Fischl, Bruce and Dalca, Adrian V.},
	month = oct,
	year = {2023},
	note = {arXiv:2303.01592 [eess]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Neurons and Cognition},
	file = {Preprint PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/RR4A8686/Li et al. - 2023 - Joint cortical registration of geometry and function using semi-supervised learning.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/58C35DE3/2303.html:text/html},
}

@article{zhao_longitudinally_2024,
	title = {Longitudinally consistent registration and parcellation of cortical surfaces using semi-supervised learning},
	volume = {96},
	issn = {1361-8415},
	url = {https://www.sciencedirect.com/science/article/pii/S136184152400118X},
	doi = {10.1016/j.media.2024.103193},
	abstract = {Temporally consistent and accurate registration and parcellation of longitudinal cortical surfaces is of great importance in studying longitudinal morphological and functional changes of human brains. However, most existing methods are developed for registration or parcellation of a single cortical surface. When applying to longitudinal studies, these methods independently register/parcellate each surface from longitudinal scans, thus often generating longitudinally inconsistent and inaccurate results, especially in small or ambiguous cortical regions. Essentially, longitudinal cortical surface registration and parcellation are highly correlated tasks with inherently shared constraints on both spatial and temporal feature representations, which are unfortunately ignored in existing methods. To this end, we unprecedentedly propose a novel semi-supervised learning framework to exploit these inherent relationships from limited labeled data and extensive unlabeled data for more robust and consistent registration and parcellation of longitudinal cortical surfaces. Our method utilizes the spherical topology characteristic of cortical surfaces. It employs a spherical network to function as an encoder, which extracts high-level cortical features. Subsequently, we build two specialized decoders dedicated to the tasks of registration and parcellation, respectively. To extract more meaningful spatial features, we design a novel parcellation map similarity loss to utilize the relationship between registration and parcellation tasks, i.e., the parcellation map warped by the deformation field in registration should match the atlas parcellation map, thereby providing extra supervision for the registration task and augmented data for parcellation task by warping the atlas parcellation map to unlabeled surfaces. To enable temporally more consistent feature representation, we additionally enforce longitudinal consistency among longitudinal surfaces after registering them together using their concatenated features. Experiments on two longitudinal datasets of infants and adults have shown that our method achieves significant improvements on both registration/parcellation accuracy and longitudinal consistency compared to existing methods, especially in small and challenging cortical regions.},
	urldate = {2025-07-09},
	journal = {Medical Image Analysis},
	author = {Zhao, Fenqiang and Wu, Zhengwang and Wang, Li and Lin, Weili and Li, Gang},
	month = aug,
	year = {2024},
	keywords = {Cortical surface registration, Longitudinal analysis, Parcellation, Spherical network},
	pages = {103193},
	file = {ScienceDirect Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/SU9HCZ3B/S136184152400118X.html:text/html},
}



@article{vos_deep_2019,
	title = {A {Deep} {Learning} {Framework} for {Unsupervised} {Affine} and {Deformable} {Image} {Registration}},
	volume = {52},
	issn = {13618415},
	url = {http://arxiv.org/abs/1809.06130},
	doi = {10.1016/j.media.2018.11.010},
	abstract = {Image registration, the process of aligning two or more images, is the core technique of many (semi-)automatic medical image analysis tasks. Recent studies have shown that deep learning methods, notably convolutional neural networks (ConvNets), can be used for image registration. Thus far training of ConvNets for registration was supervised using predefined example registrations. However, obtaining example registrations is not trivial. To circumvent the need for predefined examples, and thereby to increase convenience of training ConvNets for image registration, we propose the Deep Learning Image Registration (DLIR) framework for {\textbackslash}textit\{unsupervised\} affine and deformable image registration. In the DLIR framework ConvNets are trained for image registration by exploiting image similarity analogous to conventional intensity-based image registration. After a ConvNet has been trained with the DLIR framework, it can be used to register pairs of unseen images in one shot. We propose flexible ConvNets designs for affine image registration and for deformable image registration. By stacking multiple of these ConvNets into a larger architecture, we are able to perform coarse-to-fine image registration. We show for registration of cardiac cine MRI and registration of chest CT that performance of the DLIR framework is comparable to conventional image registration while being several orders of magnitude faster.},
	urldate = {2025-07-09},
	journal = {Medical Image Analysis},
	author = {Vos, Bob D. de and Berendsen, Floris F. and Viergever, Max A. and Sokooti, Hessam and Staring, Marius and Isgum, Ivana},
	month = feb,
	year = {2019},
	note = {arXiv:1809.06130 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	pages = {128--143},
	file = {Preprint PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/NF4MLMNX/Vos et al. - 2019 - A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/N8PNIPU4/1809.html:text/html},
}

@article{balakrishnan_voxelmorph_2019,
	title = {{VoxelMorph}: {A} {Learning} {Framework} for {Deformable} {Medical} {Image} {Registration}},
	volume = {38},
	issn = {0278-0062, 1558-254X},
	shorttitle = {{VoxelMorph}},
	url = {http://arxiv.org/abs/1809.05231},
	doi = {10.1109/TMI.2019.2897538},
	abstract = {We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical image registration. Traditional registration methods optimize an objective function for each pair of images, which can be time-consuming for large datasets or rich deformation models. In contrast to this approach, and building on recent learning-based methods, we formulate registration as a function that maps an input image pair to a deformation field that aligns these images. We parameterize the function via a convolutional neural network (CNN), and optimize the parameters of the neural network on a set of images. Given a new pair of scans, VoxelMorph rapidly computes a deformation field by directly evaluating the function. In this work, we explore two different training strategies. In the first (unsupervised) setting, we train the model to maximize standard image matching objective functions that are based on the image intensities. In the second setting, we leverage auxiliary segmentations available in the training data. We demonstrate that the unsupervised model's accuracy is comparable to state-of-the-art methods, while operating orders of magnitude faster. We also show that VoxelMorph trained with auxiliary data improves registration accuracy at test time, and evaluate the effect of training set size on registration. Our method promises to speed up medical image analysis and processing pipelines, while facilitating novel directions in learning-based registration and its applications. Our code is freely available at voxelmorph.csail.mit.edu.},
	number = {8},
	urldate = {2025-07-09},
	journal = {IEEE Trans. Med. Imaging},
	author = {Balakrishnan, Guha and Zhao, Amy and Sabuncu, Mert R. and Guttag, John and Dalca, Adrian V.},
	month = aug,
	year = {2019},
	note = {arXiv:1809.05231 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	pages = {1788--1800},
	file = {Preprint PDF:/home/localaak22/snap/zotero-snap/common/Zotero/storage/M885B5FV/Balakrishnan et al. - 2019 - VoxelMorph A Learning Framework for Deformable Medical Image Registration.pdf:application/pdf;Snapshot:/home/localaak22/snap/zotero-snap/common/Zotero/storage/EJT3GJYS/1809.html:text/html},
}


@article{ma_developing_2025,
	title = {The {Developing} {Human} {Connectome} {Project}: {A} fast deep learning-based pipeline for neonatal cortical surface reconstruction},
	volume = {100},
	issn = {1361-8415},
	url = {https://www.sciencedirect.com/science/article/pii/S1361841524003190},
	doi = {https://doi.org/10.1016/j.media.2024.103394},
	abstract = {The Developing Human Connectome Project (dHCP) aims to explore developmental patterns of the human brain during the perinatal period. An automated processing pipeline has been developed to extract high-quality cortical surfaces from structural brain magnetic resonance (MR) images for the dHCP neonatal dataset. However, the current implementation of the pipeline requires more than 6.5 h to process a single MRI scan, making it expensive for large-scale neuroimaging studies. In this paper, we propose a fast deep learning (DL) based pipeline for dHCP neonatal cortical surface reconstruction, incorporating DL-based brain extraction, cortical surface reconstruction and spherical projection, as well as GPU-accelerated cortical surface inflation and cortical feature estimation. We introduce a multiscale deformation network to learn diffeomorphic cortical surface reconstruction end-to-end from T2-weighted brain MRI. A fast unsupervised spherical mapping approach is integrated to minimize metric distortions between cortical surfaces and projected spheres. The entire workflow of our DL-based dHCP pipeline completes within only 24 s on a modern GPU, which is nearly 1000 times faster than the original dHCP pipeline. The qualitative assessment demonstrates that for 82.5\% of the test samples, the cortical surfaces reconstructed by our DL-based pipeline achieve superior (54.2\%) or equal (28.3\%) surface quality compared to the original dHCP pipeline.},
	journal = {Medical Image Analysis},
	author = {Ma, Qiang and Liang, Kaili and Li, Liu and Masui, Saga and Guo, Yourong and Nosarti, Chiara and Robinson, Emma C. and Kainz, Bernhard and Rueckert, Daniel},
	year = {2025},
	keywords = {Cortical surface reconstruction, Deep learning, Neonatal brain MRI, Neuroimage pipeline, The developing human connectome project},
	pages = {103394},
}



@article{makropoulos_developing_2018,
	title = {The developing human connectome project: {A} minimal processing pipeline for neonatal cortical surface reconstruction},
	volume = {173},
	issn = {1095-9572},
	shorttitle = {The developing human connectome project},
	doi = {10.1016/j.neuroimage.2018.01.054},
	abstract = {The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85\% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2\% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.},
	language = {eng},
	journal = {Neuroimage},
	author = {Makropoulos, Antonios and Robinson, Emma C. and Schuh, Andreas and Wright, Robert and Fitzgibbon, Sean and Bozek, Jelena and Counsell, Serena J. and Steinweg, Johannes and Vecchiato, Katy and Passerat-Palmbach, Jonathan and Lenz, Gregor and Mortari, Filippo and Tenev, Tencho and Duff, Eugene P. and Bastiani, Matteo and Cordero-Grande, Lucilio and Hughes, Emer and Tusor, Nora and Tournier, Jacques-Donald and Hutter, Jana and Price, Anthony N. and Teixeira, Rui Pedro A. G. and Murgasova, Maria and Victor, Suresh and Kelly, Christopher and Rutherford, Mary A. and Smith, Stephen M. and Edwards, A. David and Hajnal, Joseph V. and Jenkinson, Mark and Rueckert, Daniel},
	month = jun,
	year = {2018},
	pmid = {29409960},
	pmcid = {PMC6783314},
	keywords = {Brain, Connectome, Cortical surface reconstruction, Developing human connectome project, dHCP, Female, Humans, Image Processing, Computer-Assisted, Infant, Newborn, Magnetic Resonance Imaging, Male, Neonatal MRI, Pipeline, Segmentation},
	pages = {88--112},
	file = {Full Text:/home/localaak22/snap/zotero-snap/common/Zotero/storage/2H7VLWPK/Makropoulos et al. - 2018 - The developing human connectome project A minimal processing pipeline for neonatal cortical surface.pdf:application/pdf},
}
@article{
doi:10.1073/pnas.1715451115,
author = {Kara E. Garcia  and Emma C. Robinson  and Dimitrios Alexopoulos  and Donna L. Dierker  and Matthew F. Glasser  and Timothy S. Coalson  and Cynthia M. Ortinau  and Daniel Rueckert  and Larry A. Taber  and David C. Van Essen  and Cynthia E. Rogers  and Christopher D. Smyser  and Philip V. Bayly },
title = {Dynamic patterns of cortical expansion during folding of the preterm human brain},
journal = {Proceedings of the National Academy of Sciences},
volume = {115},
number = {12},
pages = {3156-3161},
year = {2018},
doi = {10.1073/pnas.1715451115},
URL = {https://www.pnas.org/doi/abs/10.1073/pnas.1715451115},
eprint = {https://www.pnas.org/doi/pdf/10.1073/pnas.1715451115},
abstract = {The human brain exhibits complex folding patterns that emerge during the third trimester of fetal development. Minor folds are quasi-randomly shaped and distributed. Major folds, in contrast, are more conserved and form important landmarks. Disruption of cortical folding is associated with devastating disorders of cognition and emotion. Despite decades of study, the processes that produce normal and abnormal folding remain unresolved, although the relatively rapid tangential expansion of the cortex has emerged as a driving factor. Accurate and precise measurement of cortical growth patterns during the period of folding has remained elusive. Here, we illuminate the spatiotemporal dynamics of cortical expansion by analyzing MRI-derived surfaces of preterm infant brains, using a unique strain energy minimization approach. During the third trimester of human brain development, the cerebral cortex undergoes dramatic surface expansion and folding. Physical models suggest that relatively rapid growth of the cortical gray matter helps drive this folding, and structural data suggest that growth may vary in both space (by region on the cortical surface) and time. In this study, we propose a unique method to estimate local growth from sequential cortical reconstructions. Using anatomically constrained multimodal surface matching (aMSM), we obtain accurate, physically guided point correspondence between younger and older cortical reconstructions of the same individual. From each pair of surfaces, we calculate continuous, smooth maps of cortical expansion with unprecedented precision. By considering 30 preterm infants scanned two to four times during the period of rapid cortical expansion (28–38 wk postmenstrual age), we observe significant regional differences in growth across the cortical surface that are consistent with the emergence of new folds. Furthermore, these growth patterns shift over the course of development, with noninjured subjects following a highly consistent trajectory. This information provides a detailed picture of dynamic changes in cortical growth, connecting what is known about patterns of development at the microscopic (cellular) and macroscopic (folding) scales. Since our method provides specific growth maps for individual brains, we are also able to detect alterations due to injury. This fully automated surface analysis, based on tools freely available to the brain-mapping community, may also serve as a useful approach for future studies of abnormal growth due to genetic disorders, injury, or other environmental variables.}}




@article{i__lyu_8ed77aa9,
  author = {I. Lyu AND H. Kang AND N.D. Woodward AND M.A. Styner AND B.A. Landman},
  title = {Hierarchical spherical deformation for cortical surface registration},
  journal = {Carolina Digital Repository (University of North Carolina at Chapel Hill)},
  year = {2019},
  month = {01},
  doi = {10.17615/vwav-z318},
  issn = {},
  isbn = {},
  url = {https://doi.org/10.17615/vwav-z318},
  publisher = {University of North Carolina at Chapel Hill}
}


@misc{fenqiang_zhao_124056ba,
  author = {Fenqiang Zhao AND Zhengwang Wu AND Gang Li},
  title = {Deep learning in cortical surface-based neuroimage analysis: a systematic review},
  journal = {Intelligent Medicine},
  volume = {3},
  issue = {1},
  pages = {46-58},
  year = {2022},
  month = {07},
  doi = {10.1016/j.imed.2022.06.002},
  issn = {2667-1026, 2096-9376},
  isbn = {},
  url = {https://doi.org/10.1016/j.imed.2022.06.002},
  publisher = {Elsevier BV}
}
@misc{nihms971379_pdf_3f63030e,
  author = {},
  title = {nihms971379.pdf},
  issn = {},
  isbn = {}
}
@article{jieyu_cheng_51d882c4,
  author = {Jieyu Cheng AND Adrian V. Dalca AND Bruce Fischl AND Lilla Zöllei},
  title = {Cortical surface registration using unsupervised learning},
  journal = {NeuroImage},
  volume = {221},
  pages = {117161-117161},
  year = {2020},
  month = {07},
  doi = {10.1016/j.neuroimage.2020.117161},
  pmid = {32702486},
  issn = {1053-8119, 1095-9572},
  isbn = {},
  url = {https://doi.org/10.1016/j.neuroimage.2020.117161},
  publisher = {Elsevier BV}
}
@article{jiale_cheng_c2b221f5,
  author = {Jiale Cheng AND Fenqiang Zhao AND Dan Hu AND Chao Cao AND Zhengwang Wu AND Xinrui Yuan AND Kangfu Han AND Lu Zhang AND Tianming Liu AND Dajiang Zhu AND Gang Li},
  title = {Unsupervised Cortical Surface Registration Network for Aligning Gyralnet},
  pages = {1-5},
  year = {2025},
  month = {04},
  doi = {10.1109/isbi60581.2025.10981138},
  issn = {},
  isbn = {},
  url = {https://doi.org/10.1109/isbi60581.2025.10981138}
}











@article{zhuoshuo_li_f46650cf,
  author = {Zhuoshuo Li AND Jiong Zhang AND Youbing Zeng AND Jiaying Lin AND Jiong Zhang AND Jianjia Zhang AND Duan Xu AND Hosung Kim AND Beilei Liu AND Mengting Liu},
  title = {SurfGNN: A robust surface-based prediction model with interpretability for coactivation maps of spatial and cortical features},
  journal = {Medical Image Analysis},
  volume = {107},
  pages = {103793-103793},
  year = {2025},
  month = {09},
  doi = {10.1016/j.media.2025.103793},
  pmid = {40934807},
  issn = {1361-8415, 1361-8423, 1361-8431},
  isbn = {},
  url = {https://doi.org/10.1016/j.media.2025.103793},
  publisher = {Elsevier BV}
}
@article{yu_li_67cf5e74,
  author = {Yu Li AND Xin Zhang AND Jingxin Nie AND Guowei Zhang AND Ruiyan Fang AND Xiangmin Xu AND Zhengwang Wu AND Dan Hu AND Li Wang AND Han Zhang AND Weili Lin AND Gang Li},
  title = {Brain Connectivity Based Graph Convolutional Networks and Its Application to Infant Age Prediction},
  journal = {IEEE Transactions on Medical Imaging},
  volume = {41},
  issue = {10},
  pages = {2764-2776},
  year = {2022},
  month = {05},
  doi = {10.1109/tmi.2022.3171778},
  pmid = {35500083},
  issn = {0278-0062, 1558-254X},
  isbn = {},
  url = {https://doi.org/10.1109/tmi.2022.3171778},
  publisher = {Institute of Electrical and Electronics Engineers}
}
@article{pierre_besson_35a533b0,
  author = {Pierre Besson AND Todd B. Parrish AND Aggelos K. Katsaggelos AND S. Kathleen Bandt},
  title = {Geometric deep learning on brain shape predicts sex and age},
  journal = {bioRxiv (Cold Spring Harbor Laboratory)},
  year = {2020},
  month = {06},
  doi = {10.1101/2020.06.29.177543},
  issn = {},
  isbn = {},
  url = {https://doi.org/10.1101/2020.06.29.177543},
  publisher = {Cold Spring Harbor Laboratory}
}

@article{mathilde_ripart_9358388f,
  author = {Mathilde Ripart AND Hannah Spitzer AND Logan Z. J. Williams AND Lennart Walger AND Andrew A. Chen AND Antonio Napolitano AND Maria Camilla Rossi‐Espagnet AND Stephen T. Foldes AND Wenhan Hu AND Jiajie Mo AND Marcus Likeman AND Theodor Rüber AND Maria Eugenia Caligiuri AND Antonio Gambardella AND Christopher Güttler AND Anna Tietze AND Matteo Lenge AND Renzo Guerrini AND Nathan T. Cohen AND Irène Wang AND Ane Kloster AND Lars H. Pinborg AND Khalid Hamandi AND Graeme D. Jackson AND Domenico Tortora AND Martin Tisdall AND Estefanía Conde‐Blanco AND José C. Pariente AND Carmen Pérez‐Enríquez AND Sofía González‐Ortiz AND Nandini Mullatti AND Katy Vecchiato AND Yawu Liu AND Reetta Kälviäinen AND Drahoslav Sokol AND Jay Shetty AND Benjamin Sinclair AND Lucy Vivash AND Anna Willard AND Gavin P. Winston AND Clarissa Lin Yasuda AND Fernando Cendes AND Russell T. Shinohara AND John S. Duncan AND J. Helen Cross AND Torsten Baldeweg AND Emma C. Robinson AND Juan Eugenio Iglesias AND Sophie Adler AND Konrad Wagstyl AND Abdulah Fawaz AND Alessandro De Benedictis AND Luca De Palma AND Kai Zhang AND Angelo Labate AND Carmen Barba AND Xiaozhen You AND William D. Gaillard AND Yingying Tang AND Shan Wang AND Shirin Davies AND Mira Semmelroch AND Mariasavina Severino AND Pasquale Striano AND Ajai Chari AND Felice D’Arco AND Kshitij Mankad AND Núria Bargalló AND Saül Pascual‐Diaz AND Ignacio Delgado AND Jonathan O’Muircheartaigh AND Eugenio Abela AND Jothy Kandasamy AND Ailsa McLellan AND Patricia Desmond AND Elaine Lui AND Terence J. O’Brien AND Kirstie Whitaker},
  title = {Detection of Epileptogenic Focal Cortical Dysplasia Using Graph Neural Networks},
  journal = {JAMA Neurology},
  volume = {82},
  issue = {4},
  pages = {397-397},
  year = {2025},
  month = {02},
  doi = {10.1001/jamaneurol.2024.5406},
  pmid = {39992650},
  issn = {2168-6149, 2168-6157},
  isbn = {},
  url = {https://doi.org/10.1001/jamaneurol.2024.5406},
  publisher = {American Medical Association}
}
@article{jiale_cheng_057cde06,
  author = {Jiale Cheng AND Xin Zhang AND Fenqiang Zhao AND Zhengwang Wu AND Xinrui Yuan AND John H. Gilmore AND Li Wang AND Weili Lin AND Gang Li},
  title = {Spherical Transformer on Cortical Surfaces},
  journal = {Lecture notes in computer science},
  pages = {406-415},
  year = {2022},
  month = {01},
  doi = {10.1007/978-3-031-21014-3\_42},
  pmid = {38107539},
  issn = {0302-9743, 1611-3349},
  isbn = {},
  url = {https://doi.org/10.1007/978-3-031-21014-3\_42},
  publisher = {Springer Science+Business Media}
}
@article{simon_dahan_65eaaf18,
  author = {Simon Dahan AND Abdulah Fawaz AND Logan Z. J. Williams AND Chunhui Yang AND Timothy S. Coalson AND Matthew F. Glasser AND A. David Edwards AND Daniel Rueckert AND Emma C. Robinson},
  title = {Surface Vision Transformers: Attention-Based Modelling applied to Cortical Analysis},
  journal = {arXiv (Cornell University)},
  year = {2022},
  month = {03},
  doi = {10.48550/arxiv.2203.16414},
  issn = {},
  isbn = {},
  url = {http://arxiv.org/abs/2203.16414},
  publisher = {Cornell University}
}

@article{shi_min_hu_dc0f9efd,
  author = {Shi‐Min Hu AND Zheng-Ning Liu AND Meng-Hao Guo AND Jun-Xiong Cai AND Jiahui Huang AND Tai‐Jiang Mu AND Ralph R. Martin},
  title = {Subdivision-based Mesh Convolution Networks},
  journal = {ACM Transactions on Graphics},
  volume = {41},
  issue = {3},
  pages = {1-16},
  year = {2022},
  month = {03},
  doi = {10.1145/3506694},
  issn = {0730-0301, 1557-7368},
  isbn = {},
  url = {https://doi.org/10.1145/3506694},
  publisher = {Association for Computing Machinery}
}
@article{nicholas_sharp_10639405,
  author = {Nicholas Sharp AND Souhaib Attaiki AND Keenan Crane AND Maks Ovsjanikov},
  title = {DiffusionNet: Discretization Agnostic Learning on Surfaces},
  journal = {arXiv (Cornell University)},
  year = {2020},
  month = {12},
  doi = {10.48550/arxiv.2012.00888},
  issn = {},
  isbn = {},
  url = {http://arxiv.org/abs/2012.00888},
  publisher = {Cornell University}
}
@article{patrick_m__jensen_9d41066e,
  author = {Patrick M. Jensen AND Udaranga Wickramasinghe AND Anders Bjorholm Dahl AND Pascal Fua AND Vedrana Andersen Dahl},
  title = {Deep Active Latent Surfaces for Medical Geometries},
  journal = {arXiv (Cornell University)},
  year = {2022},
  month = {06},
  doi = {10.48550/arxiv.2206.10241},
  issn = {},
  isbn = {},
  url = {http://arxiv.org/abs/2206.10241},
  publisher = {Cornell University}
}
@article{mathias_micheelsen_lowes_f35c05d7,
  author = {Mathias Micheelsen Lowes AND Jonas Jalili Pedersen AND Bjørn S. Hansen AND Klaus Fuglsang Kofoed AND Maxime Sermesant AND Rasmus R. Paulsen},
  title = {Implicit Neural Representations for Registration of Left Ventricle Myocardium During a Cardiac Cycle},
  pages = {172-182},
  year = {2025},
  month = {01},
  doi = {10.48550/ARXIV.2501.07248},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/2501.07248},
  publisher = {arXiv}
}
@inbook{jaume_ban_s_485b93aa,
  author = {Jaume Banús AND Antoine Delaloye AND Pedro M. Gordaliza AND Costa Georgantas AND Ruud B. van Heeswijk AND Jonas Richiardi},
  title = {NIMOSEF: Neural Implicit Motion and Segmentation Functions},
  journal = {Lecture notes in computer science},
  pages = {444-454},
  year = {2025},
  month = {09},
  doi = {10.1007/978-3-032-05141-7\_43},
  issn = {0302-9743, 1611-3349},
  isbn = {},
  url = {https://doi.org/10.1007/978-3-032-05141-7\_43},
  publisher = {Springer Science+Business Media}
}
@inbook{veronika_a__zimmer_0b42a2ea,
  author = {Veronika A. Zimmer AND Kerstin Hammernik AND Vasiliki Sideri‐Lampretsa AND Wenqi Huang AND Anna Reithmeir AND Daniel Rueckert AND Julia A. Schnabel},
  title = {Towards Generalised Neural Implicit Representations for Image Registration},
  journal = {Lecture notes in computer science},
  pages = {45-55},
  year = {2024},
  month = {01},
  doi = {10.1007/978-3-031-53767-7\_5},
  issn = {0302-9743, 1611-3349},
  isbn = {},
  url = {https://doi.org/10.1007/978-3-031-53767-7\_5},
  publisher = {Springer Science+Business Media}
}
@article{lin_tian_39284f63,
  author = {Lin Tian AND Hastings Greer AND Raúl San José Estépar AND Roni Sengupta AND Marc Niethammer},
  title = {\$\textbackslash{}textttNePhi\$: Neural Deformation Fields for Approximately Diffeomorphic Medical Image Registration},
  pages = {213-237},
  year = {2023},
  month = {01},
  doi = {10.48550/ARXIV.2309.07322},
  issn = {},
  isbn = {},
  url = {https://arxiv.org/abs/2309.07322},
  publisher = {arXiv}
}

@article{tustison_antsx_2021,
	title = {The {ANTsX} ecosystem for quantitative biological and medical imaging},
	volume = {11},
	issn = {2045-2322},
	url = {https://doi.org/10.1038/s41598-021-87564-6},
	doi = {10.1038/s41598-021-87564-6},
	abstract = {The Advanced Normalizations Tools ecosystem, known as ANTsX, consists of multiple open-source software libraries which house top-performing algorithms used worldwide by scientific and research communities for processing and analyzing biological and medical imaging data. The base software library, ANTs, is built upon, and contributes to, the NIH-sponsored Insight Toolkit. Founded in 2008 with the highly regarded Symmetric Normalization image registration framework, the ANTs library has since grown to include additional functionality. Recent enhancements include statistical, visualization, and deep learning capabilities through interfacing with both the R statistical project (ANTsR) and Python (ANTsPy). Additionally, the corresponding deep learning extensions ANTsRNet and ANTsPyNet (built on the popular TensorFlow/Keras libraries) contain several popular network architectures and trained models for specific applications. One such comprehensive application is a deep learning analog for generating cortical thickness data from structural T1-weighted brain MRI, both cross-sectionally and longitudinally. These pipelines significantly improve computational efficiency and provide comparable-to-superior accuracy over multiple criteria relative to the existing ANTs workflows and simultaneously illustrate the importance of the comprehensive ANTsX approach as a framework for medical image analysis.},
	number = {1},
	journal = {Scientific Reports},
	author = {Tustison, Nicholas J. and Cook, Philip A. and Holbrook, Andrew J. and Johnson, Hans J. and Muschelli, John and Devenyi, Gabriel A. and Duda, Jeffrey T. and Das, Sandhitsu R. and Cullen, Nicholas C. and Gillen, Daniel L. and Yassa, Michael A. and Stone, James R. and Gee, James C. and Avants, Brian B.},
	month = apr,
	year = {2021},
	pages = {9068},
}

@article{KUKLISOVAMURGASOVA20121550,
title = {Reconstruction of fetal brain MRI with intensity matching and complete outlier removal},
journal = {Medical Image Analysis},
volume = {16},
number = {8},
pages = {1550-1564},
year = {2012},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2012.07.004},
url = {https://www.sciencedirect.com/science/article/pii/S1361841512000965},
author = {Maria Kuklisova-Murgasova and Gerardine Quaghebeur and Mary A. Rutherford and Joseph V. Hajnal and Julia A. Schnabel},
keywords = {Fetal MRI, 3D reconstruction, Super-resolution, Bias field, Intensity matching},
abstract = {We propose a method for the reconstruction of volumetric fetal MRI from 2D slices, comprising super-resolution reconstruction of the volume interleaved with slice-to-volume registration to correct for the motion. The method incorporates novel intensity matching of acquired 2D slices and robust statistics which completely excludes identified misregistered or corrupted voxels and slices. The reconstruction method is applied to motion-corrupted data simulated from MRI of a preterm neonate, as well as 10 clinically acquired thick-slice fetal MRI scans and three scan-sequence optimized thin-slice fetal datasets. The proposed method produced high quality reconstruction results from all the datasets to which it was applied. Quantitative analysis performed on simulated and clinical data shows that both intensity matching and robust statistics result in statistically significant improvement of super-resolution reconstruction. The proposed novel EM-based robust statistics also improves the reconstruction when compared to previously proposed Huber robust statistics. The best results are obtained when thin-slice data and the correct approximation of the point spread function is used. This paper addresses the need for a comprehensive reconstruction algorithm of 3D fetal MRI, so far lacking in the scientific literature.}
}


@article{cordero-grande_three-dimensional_2018,
	title = {Three-dimensional motion corrected sensitivity encoding reconstruction for multi-shot multi-slice {MRI}: {Application} to neonatal brain imaging},
	volume = {79},
	copyright = {© 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine},
	issn = {1522-2594},
	shorttitle = {Three-dimensional motion corrected sensitivity encoding reconstruction for multi-shot multi-slice {MRI}},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.26796},
	doi = {10.1002/mrm.26796},
	abstract = {Purpose To introduce a methodology for the reconstruction of multi-shot, multi-slice magnetic resonance imaging able to cope with both within-plane and through-plane rigid motion and to describe its application in structural brain imaging. Theory and Methods The method alternates between motion estimation and reconstruction using a common objective function for both. Estimates of three-dimensional motion states for each shot and slice are gradually refined by improving on the fit of current reconstructions to the partial k-space information from multiple coils. Overlapped slices and super-resolution allow recovery of through-plane motion and outlier rejection discards artifacted shots. The method is applied to T2 and T1 brain scans acquired in different views. Results The procedure has greatly diminished artifacts in a database of 1883 neonatal image volumes, as assessed by image quality metrics and visual inspection. Examples showing the ability to correct for motion and robustness against damaged shots are provided. Combination of motion corrected reconstructions for different views has shown further artifact suppression and resolution recovery. Conclusion The proposed method addresses the problem of rigid motion in multi-shot multi-slice anatomical brain scans. Tests on a large collection of potentially corrupted datasets have shown a remarkable image quality improvement. Magn Reson Med 79:1365–1376, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.},
	language = {en},
	number = {3},
	urldate = {2025-12-11},
	journal = {Magnetic Resonance in Medicine},
	author = {Cordero-Grande, Lucilio and Hughes, Emer J. and Hutter, Jana and Price, Anthony N. and Hajnal, Joseph V.},
	year = {2018},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.26796},
	keywords = {image reconstruction, magnetic resonance, motion correction, multi-shot multi-slice images, neonatal brain imaging},
	pages = {1365--1376},
	file = {Full Text PDF:/home/aak22/snap/zotero-snap/common/Zotero/storage/3KMJAYUY/Cordero-Grande et al. - 2018 - Three-dimensional motion corrected sensitivity encoding reconstruction for multi-shot multi-slice MR.pdf:application/pdf;Snapshot:/home/aak22/snap/zotero-snap/common/Zotero/storage/823MH3TZ/mrm.html:text/html},
}



@article{marcus_informatics_2011,
	title = {Informatics and {Data} {Mining} {Tools} and {Strategies} for the {Human} {Connectome} {Project}},
	volume = {5},
	issn = {1662-5196},
	url = {https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2011.00004/full},
	doi = {10.3389/fninf.2011.00004},
	abstract = {The Human Connectome Project (HCP) is a major endeavor that will acquire and analyze connectivity data plus other neuroimaging, behavioral, and genetic data from 1,200 healthy adults. It will serve as a key resource for the neuroscience research community, enabling discoveries of how the brain is wired and how it functions in different individuals. To fulfill its potential, the HCP consortium is developing an informatics platform that will handle: 1) storage of primary and processed data, 2) systematic processing and analysis of the data, 3) open access data sharing, and 4) mining and exploration of the data. This informatics platform will include two primary components. ConnectomeDB will provide database services for storing and distributing the data, as well as data analysis pipelines. Connectome Workbench will provide visualization and exploration capabilities. The platform will be based on standard data formats and provide an open set of application programming interfaces (APIs) that will facilitate broad utilization of the data and integration of HCP services into a variety of external applications. Primary and processed data generated by the HCP will be openly shared with the scientific community, and the informatics platform will be available under an open source license. This paper describes the HCP informatics platform as currently envisioned and places it into the context of the overall HCP vision and agenda.},
	language = {English},
	urldate = {2025-12-11},
	journal = {Frontiers in Neuroinformatics},
	author = {Marcus, Daniel and Harwell, John and Olsen, Timothy and Hodge, Michael and Glasser, Matthew and Prior, Fred and Jenkinson, Mark and Laumann, Timothy and Curtiss, Sandra and Van Essen, David},
	month = jun,
	year = {2011},
	note = {Publisher: Frontiers},
	keywords = {brain parcellation, caret, connectomics, diffusion imaging, human connectome project, Network analysis, resting state fMRI, XNAT},
	file = {Full Text PDF:/home/aak22/snap/zotero-snap/common/Zotero/storage/HTYRFMRR/Marcus et al. - 2011 - Informatics and Data Mining Tools and Strategies for the Human Connectome Project.pdf:application/pdf},
}
@article{alexander_desikan-killiany-tourville_2019,
	title = {Desikan-{Killiany}-{Tourville} {Atlas} {Compatible} {Version} of {M}-{CRIB} {Neonatal} {Parcellated} {Whole} {Brain} {Atlas}: {The} {M}-{CRIB} 2.0},
	volume = {13},
	issn = {1662-453X},
	shorttitle = {Desikan-{Killiany}-{Tourville} {Atlas} {Compatible} {Version} of {M}-{CRIB} {Neonatal} {Parcellated} {Whole} {Brain} {Atlas}},
	url = {https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2019.00034/full},
	doi = {10.3389/fnins.2019.00034},
	abstract = {Our recently published M-CRIB atlas comprises 100 neonatal brain regions including 68 compatible with the widely-used Desikan-Killiany adult cortical atlas. A successor to the Desikan-Killiany atlas is the Desikan-Killiany-Tourville atlas, in which some regions with unclear boundaries were removed, and many existing boundaries were revised to conform to clearer landmarks in sulcal fundi. Our first aim here was to modify cortical M-CRIB regions to comply with the Desikan-Killiany-Tourville protocol, in order to offer: a) compatibility with this adult cortical atlas, b) greater labelling accuracy due to clearer landmarks, and c) optimisation of cortical regions for integration with surface-based infant parcellation pipelines. Secondly, we aimed to update subcortical regions in order to offer greater compatibility with subcortical segmentations produced in FreeSurfer. Data utilized were the T2-weighted MRI scans in our M-CRIB atlas, for ten healthy neonates (postmenstrual age at MRI 40-43 weeks, 4 female), and corresponding parcellated images. Edits were performed on the parcellated images in volume space using ITK-SNAP. Cortical updates included deletion of frontal and temporal poles and ‘Banks STS’, and modification of boundaries of many other regions. Changes to subcortical regions included the addition of ‘ventral diencephalon’, and deletion of ‘subcortical matter’ labels. A detailed updated parcellation protocol was produced. The resulting whole-brain M-CRIB 2.0 atlas comprises 94 regions altogether. This atlas provides comparability with adult Desikan-Killiany-Tourville-labelled cortical data and FreeSurfer-labelled subcortical data, and is more readily adaptable for incorporation into surface-based neonatal parcellation pipelines. As such, it offers the ability to help facilitate a broad range of investigations into brain structure and function both at the neonatal time point and developmentally across the lifespan.},
	language = {English},
	urldate = {2025-12-11},
	journal = {Frontiers in Neuroscience},
	author = {Alexander, Bonnie and Loh, Wai Yen and Matthews, Lillian G. and Murray, Andrea L. and Adamson, Chris and Beare, Richard and Chen, Jian and Kelly, Claire E. and Anderson, Peter J. and Doyle, Lex W. and Spittle, Alicia J. and Cheong, Jeanie L. Y. and Seal, Marc L. and Thompson, Deanne K.},
	month = feb,
	year = {2019},
	note = {Publisher: Frontiers},
	keywords = {Ground truth, Infant, Manual tracing, Newborn, parcellation, segmentation},
	file = {Full Text PDF:/home/aak22/snap/zotero-snap/common/Zotero/storage/RL6Y3Z9Q/Alexander et al. - 2019 - Desikan-Killiany-Tourville Atlas Compatible Version of M-CRIB Neonatal Parcellated Whole Brain Atlas.pdf:application/pdf},
}
@article{wu_drawing_nodate,
	title = {Drawing {Developmental} {Trajectory} from {Cortical} {Surface} {Reconstruction}},
	abstract = {Diffeomorphic transformation-based cortical surface reconstruction typically involves a series of deformation processes to extract the cerebral cortex from brain magnetic resonance images (MRI). While most methods are designed for adult brains using Neural Ordinary Differential Equations (NODE) with fixed step sizes, the neonatal brain, which exhibits dramatic changes in cortical folding patterns early in life, requires a more adaptive approach. To address this, we develop a dual-task framework to directly characterize the brain development trajectory through processes of cortical surface reconstruction. For white matter (inner surfaces), we employ an Age-Conditioned ODE with adaptive step sizes. It is initially trained on a limited set of longitudinal paired data to establish a coarse trajectory, which is then refined through sample training of single-point data and knowledge distillation. For the pial surfaces (outer surfaces), we position the midthickness surfaces as intermediates and employ a cycle-consistent semisupervised training strategy to depict a coherent brain development trajectory between the inner and outer surfaces. Our approach achieves precise developmental prediction directly on triangular meshes. Furthermore, by enhancing interpretability at each stage of the deformation process, this approach improves the applicability of diffeomorphic transformation-based methods. The proposed method has demonstrated state-of-the-art performance in modeling developmental trajectories and cortical surface reconstruction within the developing Human Connectome Project dataset (dHCP). The Code will be available at https: //github.com/SCUT-Xinlab/DDT-from-CSR.},
	language = {en},
	author = {Wu, Wenxuan and Qu, Ruowen and Liu, Zhongliang and Dai, Zhuoyan and Shi, Dongzi and Yu, Sijin and Xiong, Tong and Liu, Shiping and Xu, Xiangmin and Xing, Xiaofen and Zhang, Xin},
	file = {PDF:/home/aak22/snap/zotero-snap/common/Zotero/storage/UTRX3WH9/Wu et al. - Drawing Developmental Trajectory from Cortical Surface Reconstruction.pdf:application/pdf},
}


@article{mangin_framework_2004,
	title = {A framework to study the cortical folding patterns},
	volume = {23 Suppl 1},
	issn = {1053-8119},
	doi = {10.1016/j.neuroimage.2004.07.019},
	abstract = {This paper describes a decade-long research program focused on the variability of the cortical folding patterns. The program has developed a framework of using artificial neuroanatomists that are trained to identify sulci from a database. The framework relies on a renormalization of the brain warping problem, which consists in matching the cortices at the scale of the folds. Another component of the program is the search for the alphabet of the folding patterns, namely, a list of indivisible elementary sulci. The search relies on the study of the cortical folding process using antenatal imaging and on backward simulations of morphogenesis aimed at revealing traces of the embryologic dimples in the mature cortical surface. The importance of sulcal-based morphometry is illustrated by a simple study of the correlates of handedness on asymmetry indices. The study shows for instance that the central sulcus is larger in the dominant hemisphere.},
	language = {eng},
	journal = {NeuroImage},
	author = {Mangin, J.-F. and Rivière, D. and Cachia, A. and Duchesnay, E. and Cointepas, Y. and Papadopoulos-Orfanos, D. and Scifo, P. and Ochiai, T. and Brunelle, F. and Régis, J.},
	year = {2004},
	pmid = {15501082},
	keywords = {Brain Mapping, Cerebral Cortex, Computer Simulation, Databases, Factual, Dominance, Cerebral, Female, Humans, Image Processing, Computer-Assisted, Pregnancy, Terminology as Topic},
	pages = {S129--138},
}

@article{van_essen_surface-based_2004,
	series = {Mathematics in {Brain} {Imaging}},
	title = {Surface-based approaches to spatial localization and registration in primate cerebral cortex},
	volume = {23},
	issn = {1053-8119},
	url = {https://www.sciencedirect.com/science/article/pii/S1053811904003969},
	doi = {10.1016/j.neuroimage.2004.07.024},
	abstract = {Explicit surface reconstructions provide invaluable substrates for visualizing and analyzing the complex convolutions of cerebral cortex. This report illustrates the utility of surface-based atlases of human and macaque monkey for representing many aspects of cortical organization and function. These include a variety of cortical partitioning schemes plus an open-ended collection of complex activation patterns obtained from fMRI studies. Surface-based registration from one hemisphere to an atlas provides powerful approach to (i) objectively and quantitatively representing both the consistencies and the variability of the pattern of convolutions and the patterns of functional activation from any given task; and (ii) making comparisons across species and evaluating candidate homologies between cortical areas or functionally delineated regions.},
	urldate = {2025-12-11},
	journal = {NeuroImage},
	author = {Van Essen, David C.},
	month = jan,
	year = {2004},
	keywords = {Cerebral cortex, Cortical structure, fMRI},
	pages = {S97--S107},
	file = {ScienceDirect Full Text PDF:/home/aak22/snap/zotero-snap/common/Zotero/storage/3SG56ZJK/Van Essen - 2004 - Surface-based approaches to spatial localization and registration in primate cerebral cortex.pdf:application/pdf;ScienceDirect Snapshot:/home/aak22/snap/zotero-snap/common/Zotero/storage/UYC9WCPU/S1053811904003969.html:text/html},
}
