
@PREAMBLE{ {\providecommand{\noopsort}[1]{}}}
 
@string{tip = {IEEE Trans. on Image Processing (TIP)}}
@string{tpami = {IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI)}}
@string{icip = {Proc. of IEEE International Conference on Image Processing (ICIP)}}
@string{cvpr = {Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)}} 
@string{nimg = {NeuroImage (NIMG)}}
@string{eccv = {Proc. of European Conference on Computer Vision (ECCV)}}
@string{accv = {Proc. of Asian Conference on Computer Vision (ACCV)}}
@string{iccv = {Proc. of IEEE International Conference on Computer Vision (ICCV)}}
@string{miccai = {Proc. of Int. Conf. on Medical Image Computing and Computer-Assisted Intervention (MICCAI)}}
@string{patchmi = {Proc. of Int. Work. on Patch-based Techniques in Medical Imaging (Patch-MI, MICCAI)}}
@string{mia = {Medical Image Analysis (MIA)}}
@string{isbi = {International Symposium on Biomedical Imaging (ISBI)}}

@article{fukutomi_bbs_2023,
author = {Fukutomi, Hikaru and Yamamoto, Takayuki and Sibon, Igor and Christensen, Soren and Raposo, Nicolas and Marnat, Gaultier and Albucher, Jean-Fran\c{c}ois and Olindo, St\'{e}phane and Calvi\`{e}re, Lionel and Sagnier, Sharmila and Viguier, Alain and Renou, Pauline and Guenego, Adrien and Poli, Mathilde and Darcourt, Jean and Debruxelles, Sabrina and Drif, Amel and Thalamas, Claire and Sommet, Agn\`{e}s and Rousseau, Vanessa and Mazighi, Mikael and Bonneville, Fabrice and Albers, Gregory                             W. and Cognard, Christophe and Dousset, Vincent and Olivot, Jean                             Marc and Tourdias, Thomas and },
title = {Location-weighted versus Volume-weighted Mismatch at MRI for Response                     to Mechanical Thrombectomy in Acute Stroke},
journal = {Radiology},
volume = {306},
number = {2},
pages = {e220080},
year = {2023},
doi = {10.1148/radiol.220080},
    note ={PMID: 36194114},
URL = { 
    
        https://doi.org/10.1148/radiol.220080
}

}



@article{nguyen_brain_2024,
	title = {Brain structure ages—{A} new biomarker for multi-disease classification},
	volume = {45},
	issn = {1097-0193},
	language = {en},
	number = {1},
	urldate = {2024-03-06},
	journal = {Human Brain Mapping},
	author = {Nguyen, Huy-Dung and Clément, Michaël and Mansencal, Boris and Coupé, Pierrick},
	year = {2024},
	keywords = {age prediction, Alzheimer's disease, brain structure ages, deep learning, frontotemporal dementia, multi-disease classification, multiple sclerosis, Parkinson's disease, schizophrenia},
	pages = {e26558},
	
}

@InProceedings{10.1007/978-3-031-73281-2_3,
author="Longuefosse, Arthur
and Bot, Edern Le
and De Senneville, Baudouin Denis
and Giraud, R{\'e}mi
and Mansencal, Boris
and Coup{\'e}, Pierrick
and Desbarats, Pascal
and Baldacci, Fabien",
editor="Fernandez, Virginia
and Wolterink, Jelmer M.
and Wiesner, David
and Remedios, Samuel
and Zuo, Lianrui
and Casamitjana, Adri{\`a}",
title="Adapted nnU-Net: A Robust Baseline for Cross-Modality Synthesis and Medical Image Inpainting",
booktitle="Simulation and Synthesis in Medical Imaging",
year="2025",
publisher="Springer Nature Switzerland",
address="Cham",
pages="24--33",
abstract="In medical image synthesis, the development of robust and reliable baseline methods is crucial due to the complexity and variability of existing techniques. Despite advances with architectures such as GANs and diffusion models, a clear state-of-the-art has yet to be established. This paper introduces a versatile adaptation of the nnU-Net framework as a robust baseline for both cross-modality synthesis and image inpainting tasks. Known for its superior performance in segmentation challenges, nnU-Net's automatic configuration and parameter optimization capabilities have been adapted for these new applications. We evaluate this method on two use cases: pelvis MR to CT translation using the Synthrad2023 challenge dataset and local synthesis using the BraTs 2023 inpainting challenge dataset. Standard synthesis metrics -MAE, MSE, SSIM and PSNR- demonstrate that our adapted nnU-Net outperforms GAN-based methods like pix2pixHD and ranks among the best methods for both challenges. We recommend this adapted nnU-Net as a new benchmark for medical image translation and inpainting tasks, and provide our implementations for public use on GitHub.",
isbn="978-3-031-73281-2"
}

@inproceedings{
edern2025flairbrainseg,
title={{FLAIRB}rainSeg: Fine-Grained Brain Segmentation Using {FLAIR} {MRI} Only},
author={Le Bot Edern and R{\'e}mi Giraud and Boris Mansencal and Thomas Tourdias and Jose V Manjon and Pierrick Coupe},
booktitle={Medical Imaging with Deep Learning},
year={2025},
url={https://openreview.net/forum?id=zi0EIXZk7V},
}

@article{coupe2020assemblynet,
  title={{AssemblyNet: A} large ensemble of {CNNs} for {3D} whole brain {MRI} segmentation},
  author={Coup{\'e}, Pierrick and Mansencal, Boris and Cl{\'e}ment, Micha{\"e}l and Giraud, R{\'e}mi and de Senneville, Baudouin Denis and Ta, Vinh-Thong and Lepetit, Vincent and Manjon, Jos{\'e} V},
  journal={NeuroImage},
  volume={219},
  pages={117026},
  year={2020},
  publisher={Elsevier}
}

@ARTICLE{Goodkin2021-ye,
  title     = "{FLAIR-only} joint volumetric analysis of brain lesions and
               atrophy in clinically isolated syndrome ({CIS}) suggestive of
               multiple sclerosis",
  author    = "Goodkin, O and Prados, F and Vos, S B and Pemberton, H and
               Collorone, S and Hagens, M H J and Cardoso, M J and Yousry, T A
               and Thornton, J S and Sudre, C H and Barkhof, F and {MAGNIMS
               study group}",
  abstract  = "BACKGROUND: MRI assessment in multiple sclerosis (MS) focuses on
               the presence of typical white matter (WM) lesions.
               Neurodegeneration characterised by brain atrophy is recognised
               in the research field as an important prognostic factor. It is
               not routinely reported clinically, in part due to difficulty in
               achieving reproducible measurements. Automated MRI
               quantification of WM lesions and brain volume could provide
               important clinical monitoring data. In general, lesion
               quantification relies on both T1 and FLAIR input images, while
               tissue volumetry relies on T1. However, T1-weighted scans are
               not routinely included in the clinical MS protocol, limiting the
               utility of automated quantification. OBJECTIVES: We address an
               aspect of this important translational challenge by assessing
               the performance of FLAIR-only lesion and brain segmentation,
               against a conventional approach requiring multi-contrast
               acquisition. We explore whether FLAIR-only grey matter (GM)
               segmentation yields more variability in performance compared
               with two-channel segmentation; whether this is related to field
               strength; and whether the results meet a level of clinical
               acceptability demonstrated by the ability to reproduce
               established biological associations. METHODS: We used a
               multicentre dataset of subjects with a CIS suggestive of MS
               scanned at 1.5T and 3T in the same week. WM lesions were
               manually segmented by two raters, 'manual 1' guided by consensus
               reading of CIS-specific lesions and 'manual 2' by any WM
               hyperintensity. An existing brain segmentation method was
               adapted for FLAIR-only input. Automated segmentation of WM
               hyperintensity and brain volumes were performed with
               conventional (T1/T1 + FLAIR) and FLAIR-only methods. RESULTS: WM
               lesion volumes were comparable at 1.5T between 'manual 2' and
               FLAIR-only methods and at 3T between 'manual 2', T1 + FLAIR and
               FLAIR-only methods. For cortical GM volume, linear regression
               measures between conventional and FLAIR-only segmentation were
               high (1.5T: $\alpha$ = 1.029, R2 = 0.997, standard error (SE) =
               0.007; 3T: $\alpha$ = 1.019, R2 = 0.998, SE = 0.006).
               Age-associated change in cortical GM volume was a significant
               covariate in both T1 (p = 0.001) and FLAIR-only (p = 0.005)
               methods, confirming the expected relationship between age and GM
               volume for FLAIR-only segmentations. CONCLUSIONS: FLAIR-only
               automated segmentation of WM lesions and brain volumes were
               consistent with results obtained through conventional methods
               and had the ability to demonstrate biological effects in our
               study population. Imaging protocol harmonisation and validation
               with other MS phenotypes could facilitate the integration of
               automated WM lesion volume and brain atrophy analysis as
               clinical tools in radiological MS reporting.",
  journal   = "NeuroImage Clin.",
  publisher = "Elsevier BV",
  volume    =  29,
  number    =  102542,
  pages     = "102542",
  year      =  2021,
  keywords  = "Demyelinating diseases; Magnetic resonance imaging; Multiple
               sclerosis; Neurodegenerative diseases",
  copyright = "http://creativecommons.org/licenses/by/4.0/",
  language  = "en"
}

@inproceedings{
morell-ortega2024remix,
title={{REMIX}: Resolution Enhancement through Mixture of Experts},
author={Sergio Morell-Ortega and Marina Ruiz Perez and Pierrick Coupe and Jose V Manjon},
booktitle={Medical Imaging with Deep Learning},
year={2024},
}


@ARTICLE{Schmierer2019-hb,
  title     = "Towards a standard {MRI} protocol for multiple sclerosis across
               the {UK}",
  author    = "Schmierer, Klaus and Campion, Thomas and Sinclair, Audrey and
               van Hecke, Wim and Matthews, Paul M and Wattjes, Mike P",
  abstract  = "Multiple sclerosis is a chronic inflammatory demyelinating and
               degenerative disease of the central nervous system. It is the
               most common non-traumatic cause of chronic disability in young
               adults. An early and accurate diagnosis, and effective disease
               modifying treatment are key elements of optimum care for people
               with MS (pwMS). MRI has become a critical tool to confirm the
               presence of dissemination in space and time of lesions
               characteristic of inflammatory demyelination, a cornerstone of
               MS diagnosis, over and above exclusion of numerous differential
               diagnoses. In the modern era of early and highly effective DMT,
               follow-up of pwMS also relies heavily on MRI, to both confirm
               efficacy and for pharmacovigilance. Since criteria for MS rely
               heavily on MRI, an agreed standardized acquisition and reporting
               protocol enabling efficient and equitable application across the
               UK is desirable. Following a recent meeting of MS experts in
               London (UK), we make recommendations for a standardized UK MRI
               protocol that captures the diagnostic phase as well as
               monitoring for safety and treatment efficacy once the diagnosis
               is established. Our views take into account issues arising from
               the (repeated) use of contrast agents as well as the advent of
               (semi-) automated tools to further optimize disease monitoring
               in pwMS.",
  journal   = "Br. J. Radiol.",
  publisher = "Oxford University Press (OUP)",
  volume    =  92,
  number    =  1101,
  pages     = "20180926",
  month     =  sep,
  year      =  2019,
  copyright = "https://academic.oup.com/journals/pages/open\_access/funder\_policies/chorus/standard\_publication\_model",
  language  = "en"
}

@ARTICLE{Wattjes2021-hk,
  title    = "2021 {MAGNIMS-CMSC-NAIMS} consensus recommendations on the use of
              {MRI} in patients with multiple sclerosis",
  author   = "Wattjes, Mike P and Ciccarelli, Olga and Reich, Daniel S and
              Banwell, Brenda and de Stefano, Nicola and Enzinger, Christian
              and Fazekas, Franz and Filippi, Massimo and Frederiksen, Jette
              and Gasperini, Claudio and Hacohen, Yael and Kappos, Ludwig and
              Li, David K B and Mankad, Kshitij and Montalban, Xavier and
              Newsome, Scott D and Oh, Jiwon and Palace, Jacqueline and Rocca,
              Maria A and Sastre-Garriga, Jaume and Tintor{\'e}, Mar and
              Traboulsee, Anthony and Vrenken, Hugo and Yousry, Tarek and
              Barkhof, Frederik and Rovira, {\`A}lex and {Magnetic Resonance
              Imaging in Multiple Sclerosis study group} and {Consortium of
              Multiple Sclerosis Centres} and {North American Imaging in
              Multiple Sclerosis Cooperative MRI guidelines working group}",
  journal  = "Lancet Neurol",
  volume   =  20,
  number   =  8,
  pages    = "653--670",
  month    =  jun,
  year     =  2021,
  address  = "England",
  language = "en"
}


@article{manjon2016volbrain,
  title={{volBrain:} an online {MRI} brain volumetry system},
  author={Manj{\'o}n, Jos{\'e} V and Coup{\'e}, Pierrick},
  journal={Frontiers in neuroinformatics},
  volume={10},
  pages={30},
  year={2016},
  publisher={Frontiers Media SA}
}

@InProceedings{insensee2024nnunetrevisited,
author="Isensee, Fabian
and Wald, Tassilo
and Ulrich, Constantin
and Baumgartner, Michael
and Roy, Saikat
and Maier-Hein, Klaus
and J{\"a}ger, Paul F.",
editor="Linguraru, Marius George
and Dou, Qi
and Feragen, Aasa
and Giannarou, Stamatia
and Glocker, Ben
and Lekadir, Karim
and Schnabel, Julia A.",
title="nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024",
year="2024",
publisher="Springer Nature Switzerland",
address="Cham",
pages="488--498",
isbn="978-3-031-72114-4"
}


@article{hernandez_petzsche_isles_2022,
	title = {{ISLES} 2022: {A} multi-center magnetic resonance imaging stroke lesion segmentation dataset},
	volume = {9},
	issn = {2052-4463},
	url = {https://doi.org/10.1038/s41597-022-01875-5},
	doi = {10.1038/s41597-022-01875-5},
	abstract = {Magnetic resonance imaging (MRI) is an important imaging modality in stroke. Computer based automated medical image processing is increasingly finding its way into clinical routine. The Ischemic Stroke Lesion Segmentation (ISLES) challenge is a continuous effort to develop and identify benchmark methods for acute and sub-acute ischemic stroke lesion segmentation. Here we introduce an expert-annotated, multicenter MRI dataset for segmentation of acute to subacute stroke lesions (https://doi.org/10.5281/zenodo.7153326). This dataset comprises 400 multi-vendor MRI cases with high variability in stroke lesion size, quantity and location. It is split into a training dataset of n = 250 and a test dataset of n = 150. All training data is publicly available. The test dataset will be used for model validation only and will not be released to the public. This dataset serves as the foundation of the ISLES 2022 challenge (https://www.isles-challenge.org/) with the goal of finding algorithmic methods to enable the development and benchmarking of automatic, robust and accurate segmentation methods for ischemic stroke.},
	number = {1},
	journal = {Scientific Data},
	author = {Hernandez Petzsche, Moritz R. and de la Rosa, Ezequiel and Hanning, Uta and Wiest, Roland and Valenzuela, Waldo and Reyes, Mauricio and Meyer, Maria and Liew, Sook-Lei and Kofler, Florian and Ezhov, Ivan and Robben, David and Hutton, Alexandre and Friedrich, Tassilo and Zarth, Teresa and Bürkle, Johannes and Baran, The Anh and Menze, Björn and Broocks, Gabriel and Meyer, Lukas and Zimmer, Claus and Boeckh-Behrens, Tobias and Berndt, Maria and Ikenberg, Benno and Wiestler, Benedikt and Kirschke, Jan S.},
	month = dec,
	year = {2022},
	pages = {762},
}


@inproceedings{hatamizadeh2021swin,
  title={Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images},
  author={Hatamizadeh, Ali and Nath, Vishwesh and Tang, Yucheng and Yang, Dong and Roth, Holger R and Xu, Daguang},
  booktitle={International MICCAI brainlesion workshop},
  pages={272--284},
  year={2021},
  organization={Springer}
}

@article{FONOV2011313,
title = {Unbiased average age-appropriate atlases for pediatric studies},
journal = {NeuroImage},
volume = {54},
number = {1},
pages = {313-327},
year = {2011},
issn = {1053-8119},
author = {Vladimir Fonov and Alan C. Evans and Kelly Botteron and C. Robert Almli and Robert C. McKinstry and D. Louis Collins},
keywords = {Atlas template, Registration, Pediatric image analysis}}

@article{bycroft2018uk,
  title={The {UK Biobank} resource with deep phenotyping and genomic data},
  author={Bycroft, Clare and Freeman, Colin and Petkova, Desislava and Band, Gavin and Elliott, Lloyd T and Sharp, Kevin and Motyer, Allan and Vukcevic, Damjan and Delaneau, Olivier and O’Connell, Jared and others},
  journal={Nature},
  volume={562},
  number={7726},
  pages={203--209},
  year={2018},
  publisher={Nature Publishing Group}
}

@article{vukusic2020observatoire,
  title={Observatoire Fran{\c{c}}ais de la Scl{\'e}rose en Plaques ({OFSEP}) : A unique multimodal nationwide {MS} registry in {France}},
  author={Vukusic, Sandra and Casey, Romain and Rollot, Fabien and Brochet, Bruno and Pelletier, Jean and Laplaud, David-Axel and De S{\`e}ze, J{\'e}r{\^o}me and Cotton, Fran{\c{c}}ois and Moreau, Thibault and Stankoff, Bruno and others},
  journal={Multiple Sclerosis Journal},
  volume={26},
  number={1},
  pages={118--122},
  year={2020},
  publisher={SAGE Publications Sage UK: London, England}
}

@article{puonti2016fast,
  title={Fast and sequence-adaptive whole-brain segmentation using parametric Bayesian modeling},
  author={Puonti, Oula and Iglesias, Juan Eugenio and Van Leemput, Koen},
  journal={NeuroImage},
  volume={143},
  pages={235--249},
  year={2016},
  publisher={Elsevier}
}


@article{billot2023synthseg,
  title={{SynthSeg: S}egmentation of brain {MRI} scans of any contrast and resolution without retraining},
  author={Billot, Benjamin and Greve, Douglas N and Puonti, Oula and Thielscher, Axel and Van Leemput, Koen and Fischl, Bruce and Dalca, Adrian V and Iglesias, Juan Eugenio and others},
  journal={Medical Image Analysis},
  volume={86},
  pages={102789},
  year={2023},
  publisher={Elsevier}
}


@article{billot2023robust,
	title = {{Robust} machine learning segmentation for large-scale analysis of heterogeneous clinical brain {MRI} datasets},
	author = {Billot, Benjamin and Colin, Magdamo Cheng, You and Das, Sudeshna and Iglesias, Juan Eugenio},
	journal = {{Proceedings} of the {National} {Academy} of {Sciences} ({PNAS})},
	year = {2023},
	volume = {120},
	number = {9},
	pages = {1--10}
}

@article{isensee2021nnu,
  title={{nnU-Net: A} self-configuring method for deep learning-based biomedical image segmentation},
  author={Isensee, Fabian and Jaeger, Paul F and Kohl, Simon AA and Petersen, Jens and Maier-Hein, Klaus H},
  journal={Nature methods},
  volume={18},
  number={2},
  pages={203--211},
  year={2021},
  publisher={Nature Publishing Group}
}

@article{de2020regqcnet,
  title={{RegQCNET: D}eep quality control for image-to-template brain {MRI} affine registration},
  author={Denis de Senneville, Baudouin and Manjon, Jose V and Coup{\'e}, Pierrick},
  journal={Physics in Medicine \& Biology},
  volume={65},
  number={22},
  pages={225022},
  year={2020},
  publisher={IOP Publishing}
}

@article{huo20193d,
  title={{3D} whole brain segmentation using spatially localized atlas network tiles},
  author={Huo, Yuankai and Xu, Zhoubing and Xiong, Yunxi and Aboud, Katherine and Parvathaneni, Prasanna and Bao, Shunxing and Bermudez, Camilo and Resnick, Susan M and Cutting, Laurie E and Landman, Bennett A},
  journal={NeuroImage},
  volume={194},
  pages={105--119},
  year={2019},
  publisher={Elsevier}
}


@article{yu2023unest,
  title={{UNesT: L}ocal spatial representation learning with hierarchical transformer for efficient medical segmentation},
  author={Yu, Xin and Yang, Qi and Zhou, Yinchi and Cai, Leon Y and Gao, Riqiang and Lee, Ho Hin and Li, Thomas and Bao, Shunxing and Xu, Zhoubing and Lasko, Thomas A and others},
  journal={Medical Image Analysis},
  volume={90},
  pages={102939},
  year={2023},
  publisher={Elsevier}
}

@article{nishimaki2024openmap,
  title={{OpenMAP-T1: A} Rapid Deep Learning Approach to Parcellate 280 Anatomical Regions to Cover the Whole Brain},
  author={Nishimaki, Kei and Onda, Kengo and Ikuta, Kunpei and Uchida, Yuto and Mori, Susumu and Iyatomi, Hitoshi and Oishi, Kenichi},
  journal={medRxiv},
  pages={2024--01},
  year={2024},
  publisher={Cold Spring Harbor Laboratory Press}
}

@article{roy2019quicknat,
  title={{QuickNAT: A} fully convolutional network for quick and accurate segmentation of neuroanatomy},
  author={Roy, Abhijit Guha and Conjeti, Sailesh and Navab, Nassir and Wachinger, Christian and Alzheimer's Disease Neuroimaging Initiative and others},
  journal={NeuroImage},
  volume={186},
  pages={713--727},
  year={2019},
  publisher={Elsevier}
}

@article{henschel2020fastsurfer,
  title={Fastsurfer-a fast and accurate deep learning based neuroimaging pipeline},
  author={Henschel, Leonie and Conjeti, Sailesh and Estrada, Santiago and Diers, Kersten and Fischl, Bruce and Reuter, Martin},
  journal={NeuroImage},
  volume={219},
  pages={117012},
  year={2020},
  publisher={Elsevier}
}

@article{kamraoui2022deeplesionbrain,
  title={{DeepLesionBrain: T}owards a broader deep-learning generalization for multiple sclerosis lesion segmentation},
  author={Kamraoui, Reda Abdellah and Ta, Vinh-Thong and Tourdias, Thomas and Mansencal, Boris and Manjon, Jos{\'e} V and Coup{\'e}, Pierrick},
  journal={Medical Image Analysis},
  volume={76},
  pages={102312},
  year={2022},
  publisher={Elsevier}
}

@article{manjon2010improved,
  title={Improved estimates of partial volume coefficients from noisy brain {MRI} using spatial context},
  author={Manj{\'o}n, Jos{\'e} V and Tohka, Jussi and Robles, Montserrat},
  journal={NeuroImage},
  volume={53},
  number={2},
  pages={480--490},
  year={2010},
  publisher={Elsevier}
}

@article{tustison2010n4itk,
  title={{N4ITK: I}mproved {N3} bias correction},
  author={Tustison, Nicholas J and Avants, Brian B and Cook, Philip A and Zheng, Yuanjie and Egan, Alexander and Yushkevich, Paul A and Gee, James C},
  journal={IEEE Transactions on Medical Imaging},
  volume={29},
  number={6},
  pages={1310--1320},
  year={2010},
  publisher={IEEE}
}

@article{avants2011reproducible,
  title={A reproducible evaluation of {ANTs} similarity metric performance in brain image registration},
  author={Avants, Brian B and Tustison, Nicholas J and Song, Gang and Cook, Philip A and Klein, Arno and Gee, James C},
  journal={Neuroimage},
  volume={54},
  number={3},
  pages={2033--2044},
  year={2011},
  publisher={Elsevier}
}

@InProceedings{manjon2020blind,
author="Manj{\'o}n, Jos{\'e} V.
and Romero, Jos{\'e} E.
and Vivo-Hernando, Roberto
and Rubio, Gregorio
and Aparici, Fernando
and de la Iglesia-Vaya, Maria
and Tourdias, Thomas
and Coup{\'e}, Pierrick",
title="Blind MRI Brain Lesion Inpainting Using Deep Learning",
booktitle="Simulation and Synthesis in Medical Imaging",
year="2020",
pages="41--49",
abstract="In brain image analysis many of the current pipelines are not robust to the presence of lesions which degrades their accuracy and robustness. For example, performance of classic medical image processing operations such as non-linear registration or segmentation rapidly decreases when dealing with lesions. To minimize their impact, some authors have proposed to inpaint these lesions so classic pipelines can be used. However, this requires to manually delineate the regions of interest which is time consuming. In this paper, we propose a deep network that is able to blindly inpaint lesions in brain images automatically allowing current pipelines to robustly operate under pathological conditions. We demonstrate the improved robustness/accuracy in the brain segmentation problem using the SPM12 pipeline with our automatically inpainted images.",
isbn="978-3-030-59520-3"
}

@article{powers2019guidelines,
author = {William J. Powers  and Alejandro A. Rabinstein  and Teri Ackerson  and Opeolu M. Adeoye  and Nicholas C. Bambakidis  and Kyra Becker  and José Biller  and Michael Brown  and Bart M. Demaerschalk  and Brian Hoh  and Edward C. Jauch  and Chelsea S. Kidwell  and Thabele M. Leslie-Mazwi  and Bruce Ovbiagele  and Phillip A. Scott  and Kevin N. Sheth  and Andrew M. Southerland  and Deborah V. Summers  and David L. Tirschwell},
title = {Guidelines for the Early Management of Patients With Acute Ischemic Stroke: 2019 Update to the 2018 Guidelines for the Early Management of Acute Ischemic Stroke: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association},
journal = {Stroke},
volume = {50},
number = {12},
pages = {e344-e418},
year = {2019}
}

@ARTICLE{prescott2013-tm,
  title    = "Quantitative imaging biomarkers: the application of advanced
              image processing and analysis to clinical and preclinical
              decision making",
  author   = "Prescott, Jeffrey William",
  abstract = "The importance of medical imaging for clinical decision making
              has been steadily increasing over the last four decades.
              Recently, there has also been an emphasis on medical imaging for
              preclinical decision making, i.e., for use in pharamaceutical and
              medical device development. There is also a drive towards
              quantification of imaging findings by using quantitative imaging
              biomarkers, which can improve sensitivity, specificity, accuracy
              and reproducibility of imaged characteristics used for diagnostic
              and therapeutic decisions. An important component of the
              discovery, characterization, validation and application of
              quantitative imaging biomarkers is the extraction of information
              and meaning from images through image processing and subsequent
              analysis. However, many advanced image processing and analysis
              methods are not applied directly to questions of clinical
              interest, i.e., for diagnostic and therapeutic decision making,
              which is a consideration that should be closely linked to the
              development of such algorithms. This article is meant to address
              these concerns. First, quantitative imaging biomarkers are
              introduced by providing definitions and concepts. Then, potential
              applications of advanced image processing and analysis to areas
              of quantitative imaging biomarker research are described;
              specifically, research into osteoarthritis (OA), Alzheimer's
              disease (AD) and cancer is presented. Then, challenges in
              quantitative imaging biomarker research are discussed. Finally, a
              conceptual framework for integrating clinical and preclinical
              considerations into the development of quantitative imaging
              biomarkers and their computer-assisted methods of extraction is
              presented.",
  journal  = "J Digit Imaging",
  volume   =  26,
  number   =  1,
  pages    = "97--108",
  year     =  2013,
  address  = "United States",
  language = "en"
}

@article{AVANTS20112033,
title = {A reproducible evaluation of ANTs similarity metric performance in brain image registration},
journal = {NeuroImage},
volume = {54},
number = {3},
pages = {2033-2044},
year = {2011},
issn = {1053-8119},
doi = {https://doi.org/10.1016/j.neuroimage.2010.09.025},
author = {Brian B. Avants and Nicholas J. Tustison and Gang Song and Philip A. Cook and Arno Klein and James C. Gee},
}


@article{coupe_lifespan_2023,
	title = {Lifespan neurodegeneration of the human brain in multiple sclerosis},
	volume = {44},
	copyright = {© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.},
	issn = {1097-0193},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.26464},
	doi = {10.1002/hbm.26464},
	abstract = {Atrophy related to multiple sclerosis (MS) has been found at the early stages of the disease. However, the archetype dynamic trajectories of the neurodegenerative process, even prior to clinical diagnosis, remain unknown. We modeled the volumetric trajectories of brain structures across the entire lifespan using 40,944 subjects (38,295 healthy controls and 2649 MS patients). Then, we estimated the chronological progression of MS by assessing the divergence of lifespan trajectories between normal brain charts and MS brain charts. Chronologically, the first affected structure was the thalamus, then the putamen and the pallidum (around 4 years later), followed by the ventral diencephalon (around 7 years after thalamus) and finally the brainstem (around 9 years after thalamus). To a lesser extent, the anterior cingulate gyrus, insular cortex, occipital pole, caudate and hippocampus were impacted. Finally, the precuneus and accumbens nuclei exhibited a limited atrophy pattern. Subcortical atrophy was more pronounced than cortical atrophy. The thalamus was the most impacted structure with a very early divergence in life. Our experiments showed that lifespan models of most impacted structures could be an important tool for future preclinical/prodromal prognosis and monitoring of MS.},
	language = {en},
	number = {17},
	urldate = {2024-03-06},
	journal = {Human Brain Mapping},
	author = {Coupé, Pierrick and Planche, Vincent and Mansencal, Boris and Kamroui, Reda A. and Koubiyr, Ismail and Manjòn, José V. and Tourdias, Thomas},
	year = {2023},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/hbm.26464},
	keywords = {atrophy, lifespan, MRI, multiple sclerosis, staging, thalamus},
	pages = {5602--5611},
	file = {Full Text PDF:C\:\\Users\\Edern\\.zotero\\storage\\S34WJ3K8\\Coupé et al. - 2023 - Lifespan neurodegeneration of the human brain in m.pdf:application/pdf;Snapshot:C\:\\Users\\Edern\\.zotero\\storage\\HZ39NGJ3\\hbm.html:text/html},
}

