@String(PAMI = {IEEE Trans. Pattern Anal. Mach. Intell.})
@String(IJCV = {Int. J. Comput. Vis.})
@String(CVPR= {IEEE Conf. Comput. Vis. Pattern Recog.})
@String(ICCV= {Int. Conf. Comput. Vis.})
@String(ECCV= {Eur. Conf. Comput. Vis.})
@String(NIPS= {Adv. Neural Inform. Process. Syst.})
@String(ICPR = {Int. Conf. Pattern Recog.})
@String(BMVC= {Brit. Mach. Vis. Conf.})
@String(TOG= {ACM Trans. Graph.})
@String(TIP  = {IEEE Trans. Image Process.})
@String(TVCG  = {IEEE Trans. Vis. Comput. Graph.})
@String(TMM  = {IEEE Trans. Multimedia})
@String(ACMMM= {ACM Int. Conf. Multimedia})
@String(ICME = {Int. Conf. Multimedia and Expo})
@String(ICASSP=	{ICASSP})
@String(ICIP = {IEEE Int. Conf. Image Process.})
@String(ACCV  = {ACCV})
@String(ICLR = {Int. Conf. Learn. Represent.})
@String(IJCAI = {IJCAI})
@String(PR   = {Pattern Recognition})
@String(AAAI = {AAAI})
@String(CVPRW= {IEEE Conf. Comput. Vis. Pattern Recog. Worksh.})
@String(CSVT = {IEEE Trans. Circuit Syst. Video Technol.})

@String(SPL	= {IEEE Sign. Process. Letters})
@String(VR   = {Vis. Res.})
@String(JOV	 = {J. Vis.})
@String(TVC  = {The Vis. Comput.})
@String(JCST  = {J. Comput. Sci. Tech.})
@String(CGF  = {Comput. Graph. Forum})
@String(CVM = {Computational Visual Media})


@String(PAMI  = {IEEE TPAMI})
@String(IJCV  = {IJCV})
@String(CVPR  = {CVPR})
@String(ICCV  = {ICCV})
@String(ECCV  = {ECCV})
@String(NIPS  = {NeurIPS})
@String(ICPR  = {ICPR})
@String(BMVC  =	{BMVC})
@String(TOG   = {ACM TOG})
@String(TIP   = {IEEE TIP})
@String(TVCG  = {IEEE TVCG})
@String(TCSVT = {IEEE TCSVT})
@String(TMM   =	{IEEE TMM})
@String(ACMMM = {ACM MM})
@String(ICME  =	{ICME})
@String(ICASSP=	{ICASSP})
@String(ICIP  = {ICIP})
@String(ACCV  = {ACCV})
@String(ICLR  = {ICLR})
@String(IJCAI = {IJCAI})
@String(PR = {PR})
@String(AAAI = {AAAI})
@String(CVPRW= {CVPRW})
@String(CSVT = {IEEE TCSVT})

%--------------------- deep learning stuff ------------------------
@misc{tetteh2019_deepvesselnet,
      title={DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes}, 
      author={Giles Tetteh and Velizar Efremov and Nils D. Forkert and Matthias Schneider and Jan Kirschke and Bruno Weber and Claus Zimmer and Marie Piraud and Bjoern H. Menze},
      year={2019},
      eprint={1803.09340},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1803.09340}, 
}
@InProceedings{jena2021ssl,
author="Jena, Rohit
and Singla, Sumedha
and Batmanghelich, Kayhan",
editor="de Bruijne, Marleen
and Cattin, Philippe C.
and Cotin, St{\'e}phane
and Padoy, Nicolas
and Speidel, Stefanie
and Zheng, Yefeng
and Essert, Caroline",
title="Self-supervised Vessel Enhancement Using Flow-Based Consistencies",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2021",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="242--251",
abstract="Vessel segmentation is an essential task in many clinical applications. Although supervised methods have achieved state-of-art performance, acquiring expert annotation is laborious and mostly limited for two-dimensional datasets with a small sample size. On the contrary, unsupervised methods rely on handcrafted features to detect tube-like structures such as vessels. However, those methods require complex pipelines involving several hyper-parameters and design choices rendering the procedure sensitive, dataset-specific, and not generalizable. We propose a self-supervised method with a limited number of hyper-parameters that is generalizable across modalities. Our method uses tube-like structure properties, such as connectivity, profile consistency, and bifurcation, to introduce inductive bias into a learning algorithm. To model those properties, we generate a vector field that we refer to as a flow. Our experiments on various public datasets in 2D and 3D show that our method performs better than unsupervised methods while learning useful transferable features from unlabeled data. Unlike generic self-supervised methods, the learned features learn vessel-relevant features that are transferable for supervised approaches, which is essential when the number of annotated data is limited.",
isbn="978-3-030-87196-3"
}

%--------------------- solver ------------------------
@INPROCEEDINGS{gonzales2010kmst,
  author={González, Germán and Türetken, Engin and Fleuret, Franc¸ois and Fua, Pascal},
  booktitle={2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition}, 
  title={Delineating trees in noisy 2D images and 3D image-stacks}, 
  year={2010},
  volume={},
  number={},
  pages={2799-2806},
  keywords={Image coding;Image retrieval;Large-scale systems;Vocabulary;Histograms;Quantization;Helium;Image storage;Random access memory;Europe},
  doi={10.1109/CVPR.2010.5540010}}
@INPROCEEDINGS{sato2000teasar,
  author={Sato, M. and Bitter, I. and Bender, M.A. and Kaufman, A.E. and Nakajima, M.},
  booktitle={Proceedings the Eighth Pacific Conference on Computer Graphics and Applications}, 
  title={TEASAR: tree-structure extraction algorithm for accurate and robust skeletons}, 
  year={2000},
  volume={},
  number={},
  pages={281-449},
  keywords={Robustness;Skeleton;Length measurement;Volume measurement;Navigation;Humans;Colon;Automatic control;Cameras;Data preprocessing},
  doi={10.1109/PCCGA.2000.883951}}

@article{lee1994,
title = {Building Skeleton Models via 3-D Medial Surface Axis Thinning Algorithms},
journal = {CVGIP: Graphical Models and Image Processing},
volume = {56},
number = {6},
pages = {462-478},
year = {1994},
issn = {1049-9652},
doi = {https://doi.org/10.1006/cgip.1994.1042},
url = {https://www.sciencedirect.com/science/article/pii/S104996528471042X},
author = {T.C. Lee and R.L. Kashyap and C.N. Chu},
abstract = {In this paper, we present an efficient three-dimensional (3-D) parallel thinning algorithm for extracting both the medial surfaces and the medial axes of a 3-D object (given as a 3-D binary image). A new Euler table is derived to ensure the invariance of the Euler characteristic of the object, during thinning. An octree data structure of 3 × 3 × 3 lattice points is built to examine the local connectivity. The sets of "simple" points found by different researchers are compared with the constructed set. Different definitions of "surface" points including ours are given. By preserving the topological and the geometrical conditions, our algorithm produces desirable skeletons and performs better than others in terms of noise sensitivity and speed. Pre- and postprocessors can be used to remove additional noise spurs. Its use in defect analysis of objects produced by casting and forging is discussed.}
}
%--------------------- metrics ------------------------

@InProceedings{trexplorer2024,
        author = { Naeem, Roman and Hagerman, David and Svensson, Lennart and Kahl, Fredrik},
        title = { { Trexplorer: Recurrent DETR for Topologically Correct Tree Centerline Tracking } },
        booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
        year = {2024},
        publisher = {Springer Nature Switzerland},
        volume = {LNCS 15011},
        month = {October},
        page = {744 -- 754}
}
@InProceedings{trexplorer_super2025,
author="Naeem, Roman
and Hagerman, David
and Alv{\'e}n, Jennifer
and Svensson, Lennart
and Kahl, Fredrik",
editor="Gee, James C.
and Alexander, Daniel C.
and Hong, Jaesung
and Iglesias, Juan Eugenio
and Sudre, Carole H.
and Venkataraman, Archana
and Golland, Polina
and Kim, Jong Hyo
and Park, Jinah",
title="Trexplorer Super: Topologically Correct Centerline Tree Tracking of Tubular Objects in CT Volumes",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2025",
year="2025",
publisher="Springer Nature Switzerland",
address="Cham",
pages="595--605",
abstract="Tubular tree structures, such as blood vessels and airways, are essential in human anatomy, and accurately tracking them while preserving their topology is crucial for various downstream tasks. Trexplorer is a recurrent model designed for centerline tracking in 3D medical images, but it is prone to predicting duplicate branches and terminating tracking prematurely. To address these issues, we present Trexplorer Super, an enhanced version that substantially improves performance through several novel advancements. Evaluating centerline tracking models is challenging due to the lack of public benchmark datasets. To enable thorough evaluation, we develop three centerline datasets, one synthetic and two real, each with increasing difficulty. Using these datasets, we perform a comprehensive comparison of existing state-of-the-art (SOTA) models with our approach. Trexplorer Super outperforms previous SOTA models on every dataset. Our results also highlight that strong performance on synthetic data does not necessarily translate to real datasets. The code and datasets are available at https://github.com/RomStriker/Trexplorer-Super.",
isbn="978-3-032-04984-1"
}
@InProceedings{vesselformer2024,
  title = 	 {Vesselformer: Towards Complete 3D Vessel Graph Generation from Images},
  author =       {Prabhakar, Chinmay and Shit, Suprosanna and Paetzold, Johannes C. and Ezhov, Ivan and Koner, Rajat and Li, Hongwei and Kofler, Florian Sebastian and Menze, Bjoern},
  booktitle = 	 {Medical Imaging with Deep Learning},
  pages = 	 {320--331},
  year = 	 {2024},
  editor = 	 {Oguz, Ipek and Noble, Jack and Li, Xiaoxiao and Styner, Martin and Baumgartner, Christian and Rusu, Mirabela and Heinmann, Tobias and Kontos, Despina and Landman, Bennett and Dawant, Benoit},
  volume = 	 {227},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {10--12 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v227/prabhakar24a/prabhakar24a.pdf},
  url = 	 {https://proceedings.mlr.press/v227/prabhakar24a.html},
  abstract = 	 {The reconstruction of graph representations from Images (Image-to-graph) is a frequent task, especially vessel graph extraction from biomedical images. Traditionally, this problem is tackled by a two-stage process: segmentation followed by skeletonization. However, the ambiguity in the heuristic-based pruning of the centerline graph from the skeleta makes it hard to achieve a compact yet faithful graph representation. Recently, \textit{Relationformer} proposed an end-to-end solution to extract graphs directly from images. However, it does not consider edge features, particularly radius information, which is crucial in many applications such as flow simulation. Further, Relationformer predicts only patch-based graphs. In this work, we address these two shortcomings. We propose a task-specific token, namely radius-token, which explicitly focuses on capturing radius information between two nodes. Second, we propose an efficient algorithm to infer a large 3D graph from patch inference. Finally, we show experimental results on a synthetic vessel dataset and achieve the first 3D complete graph prediction. Code is available at \url{https://github.com/****}.}
}
@article{cellpose2021,
author={Stringer, Carsen
and Wang, Tim
and Michaelos, Michalis
and Pachitariu, Marius},
title={Cellpose: a generalist algorithm for cellular segmentation},
journal={Nature Methods},
year={2021},
month={Jan},
day={01},
volume={18},
number={1},
pages={100-106},
abstract={Many biological applications require the segmentation of cell bodies, membranes and nuclei from microscopy images. Deep learning has enabled great progress on this problem, but current methods are specialized for images that have large training datasets. Here we introduce a generalist, deep learning-based segmentation method called Cellpose, which can precisely segment cells from a wide range of image types and does not require model retraining or parameter adjustments. Cellpose was trained on a new dataset of highly varied images of cells, containing over 70,000 segmented objects. We also demonstrate a three-dimensional (3D) extension of Cellpose that reuses the two-dimensional (2D) model and does not require 3D-labeled data. To support community contributions to the training data, we developed software for manual labeling and for curation of the automated results. Periodically retraining the model on the community-contributed data will ensure that Cellpose improves constantly.},
issn={1548-7105},
doi={10.1038/s41592-020-01018-x},
url={https://doi.org/10.1038/s41592-020-01018-x}
}
@article{cellpose2022,
author={Pachitariu, Marius
and Stringer, Carsen},
title={Cellpose 2.0: how to train your own model},
journal={Nature Methods},
year={2022},
month={Dec},
day={01},
volume={19},
number={12},
pages={1634-1641},
abstract={Pretrained neural network models for biological segmentation can provide good out-of-the-box results for many image types. However, such models do not allow users to adapt the segmentation style to their specific needs and can perform suboptimally for test images that are very different from the training images. Here we introduce Cellpose 2.0, a new package that includes an ensemble of diverse pretrained models as well as a human-in-the-loop pipeline for rapid prototyping of new custom models. We show that models pretrained on the Cellpose dataset can be fine-tuned with only 500--1,000 user-annotated regions of interest (ROI) to perform nearly as well as models trained on entire datasets with up to 200,000 ROI. A human-in-the-loop approach further reduced the required user annotation to 100--200 ROI, while maintaining high-quality segmentations. We provide software tools such as an annotation graphical user interface, a model zoo and a human-in-the-loop pipeline to facilitate the adoption of Cellpose 2.0.},
issn={1548-7105},
doi={10.1038/s41592-022-01663-4},
url={https://doi.org/10.1038/s41592-022-01663-4}
}
@article{voreen2009,
  title={Voreen: A Rapid-Prototyping Environment for Ray-Casting-Based Volume Visualizations},
  author={Jennis Meyer-Spradow and Timo Ropinski and J{\"o}rg Mensmann and Klaus H. Hinrichs},
  journal={IEEE Computer Graphics and Applications},
  year={2009},
  volume={29},
  pages={6-13},
  url={https://api.semanticscholar.org/CorpusID:8211514}
}
@article{metrics_reloaded2024,
author={Maier-Hein, Lena
and Reinke, Annika
and Godau, Patrick
and Tizabi, Minu D.
and Buettner, Florian
and Christodoulou, Evangelia
and Glocker, Ben
and Isensee, Fabian
and Kleesiek, Jens
and Kozubek, Michal
and Reyes, Mauricio
and Riegler, Michael A.
and Wiesenfarth, Manuel
and Kavur, A. Emre
and Sudre, Carole H.
and Baumgartner, Michael
and Eisenmann, Matthias
and Heckmann-N{\"o}tzel, Doreen
and R{\"a}dsch, Tim
and Acion, Laura
and Antonelli, Michela
and Arbel, Tal
and Bakas, Spyridon
and Benis, Arriel
and Blaschko, Matthew B.
and Cardoso, M. Jorge
and Cheplygina, Veronika
and Cimini, Beth A.
and Collins, Gary S.
and Farahani, Keyvan
and Ferrer, Luciana
and Galdran, Adrian
and van Ginneken, Bram
and Haase, Robert
and Hashimoto, Daniel A.
and Hoffman, Michael M.
and Huisman, Merel
and Jannin, Pierre
and Kahn, Charles E.
and Kainmueller, Dagmar
and Kainz, Bernhard
and Karargyris, Alexandros
and Karthikesalingam, Alan
and Kofler, Florian
and Kopp-Schneider, Annette
and Kreshuk, Anna
and Kurc, Tahsin
and Landman, Bennett A.
and Litjens, Geert
and Madani, Amin
and Maier-Hein, Klaus
and Martel, Anne L.
and Mattson, Peter
and Meijering, Erik
and Menze, Bjoern
and Moons, Karel G. M.
and M{\"u}ller, Henning
and Nichyporuk, Brennan
and Nickel, Felix
and Petersen, Jens
and Rajpoot, Nasir
and Rieke, Nicola
and Saez-Rodriguez, Julio
and S{\'a}nchez, Clara I.
and Shetty, Shravya
and van Smeden, Maarten
and Summers, Ronald M.
and Taha, Abdel A.
and Tiulpin, Aleksei
and Tsaftaris, Sotirios A.
and Van Calster, Ben
and Varoquaux, Ga{\"e}l
and J{\"a}ger, Paul F.},
title={Metrics reloaded: recommendations for image analysis validation},
journal={Nature Methods},
year={2024},
month={Feb},
day={01},
volume={21},
number={2},
pages={195-212},
abstract={Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Developed by a large international consortium in a multistage Delphi process, it is based on the novel concept of a problem fingerprint---a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), dataset and algorithm output. On the basis of the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as classification tasks at image, object or pixel level, namely image-level classification, object detection, semantic segmentation and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases.},
issn={1548-7105},
doi={10.1038/s41592-023-02151-z},
url={https://doi.org/10.1038/s41592-023-02151-z}
}
@InProceedings{unet_ronneberger2015,
author="Ronneberger, Olaf
and Fischer, Philipp
and Brox, Thomas",
editor="Navab, Nassir
and Hornegger, Joachim
and Wells, William M.
and Frangi, Alejandro F.",
title="U-Net: Convolutional Networks for Biomedical Image Segmentation",
booktitle="Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015",
year="2015",
publisher="Springer International Publishing",
address="Cham",
pages="234--241",
abstract="There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net.",
isbn="978-3-319-24574-4"
}

@article{napieczynska2024muct,
  title={$\mu$CT imaging of a multi-organ vascular fingerprint in rats},
  author={Napieczy{\'n}ska, Hanna and Kedziora, Sarah M and Haase, Nadine and M{\"u}ller, Dominik N and Heuser, Arnd and Dechend, Ralf and Kr{\"a}ker, Kristin},
  journal={Plos one},
  volume={19},
  number={10},
  pages={e0308601},
  year={2024},
  publisher={Public Library of Science San Francisco, CA USA}
}

@article{Chizat2020Faster,title={Faster Wasserstein Distance Estimation with the Sinkhorn Divergence},author={Lénaïc Chizat and Pierre Roussillon and Flavien Léger and François-Xavier Vialard and Gabriel Peyré},journal={arXiv: Optimization and Control},year={2020},doi={}}

@article{flamary2021pot,
  author  = {R{\'e}mi Flamary and Nicolas Courty and Alexandre Gramfort and Mokhtar Z. Alaya and Aur{\'e}lie Boisbunon and Stanislas Chambon and Laetitia Chapel and Adrien Corenflos and Kilian Fatras and Nemo Fournier and L{\'e}o Gautheron and Nathalie T.H. Gayraud and Hicham Janati and Alain Rakotomamonjy and Ievgen Redko and Antoine Rolet and Antony Schutz and Vivien Seguy and Danica J. Sutherland and Romain Tavenard and Alexander Tong and Titouan Vayer},
  title   = {POT: Python Optimal Transport},
  journal = {Journal of Machine Learning Research},
  year    = {2021},
  volume  = {22},
  number  = {78},
  pages   = {1-8},
  url     = {http://jmlr.org/papers/v22/20-451.html}
}

@article{Glymour2021DAGs,
    title={Tutorial on Directed Acyclic Graphs.},
    author={Maria Glymour and Medellena Jeffrey N. Martin and Jean C. Digitale},
    journal={Journal of clinical epidemiology},
    year={2021},
    doi={10.1016/j.jclinepi.2021.08.001}}

@article{Gillette2011DIAMEMetric,
  title        = {The DIADEM Metric: Comparing Multiple Reconstructions of the Same Neuron},
  author       = {Gillette, Todd A. and Brown, Keith M. and Ascoli, Giorgio A.},
  journal      = {Neuroinformatics},
  volume       = {9},
  number       = {2-3},
  pages        = {233--245},
  year         = {2011},
  publisher    = {Springer},
  doi          = {10.1007/s12021-011-9117-y}
}

@article{Bentley1975KDTree,
author = {Bentley, Jon Louis},
title = {Multidimensional binary search trees used for associative searching},
year = {1975},
issue_date = {Sept. 1975},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {18},
number = {9},
issn = {0001-0782},
url = {https://doi.org/10.1145/361002.361007},
doi = {10.1145/361002.361007},
journal = {Commun. ACM},
month = sep,
pages = {509–517},
numpages = {9},
keywords = {associative retrieval, attribute, binary search trees, binary tree insertion, information retrieval system, intersection queries, key, nearest neighbor queries, partial match queries}
}

@inproceedings{rempfler2016minimum,
  title={The minimum cost connected subgraph problem in medical image analysis},
  author={Rempfler, Markus and Andres, Bjoern and Menze, Bjoern H},
  booktitle={Medical Image Computing and Computer-Assisted Intervention-MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part III 19},
  pages={397--405},
  year={2016},
  organization={Springer}
}
@article{robben2016,
title = {Simultaneous segmentation and anatomical labeling of the cerebral vasculature},
journal = {Medical Image Analysis},
volume = {32},
pages = {201-215},
year = {2016},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2016.03.006},
url = {https://www.sciencedirect.com/science/article/pii/S1361841516300056},
author = {David Robben and Engin Türetken and Stefan Sunaert and Vincent Thijs and Guy Wilms and Pascal Fua and Frederik Maes and Paul Suetens},
keywords = {Cerebral vasculature, Segmentation, Centerline extraction, Anatomical labeling, Circle of Willis, Integer programming},
abstract = {We present a novel algorithm for the simultaneous segmentation and anatomical labeling of the cerebral vasculature. Unlike existing approaches that first attempt to obtain a good segmentation and then perform labeling, we optimize for both by simultaneously taking into account the image evidence and the prior knowledge about the geometry and connectivity of the vasculature. This is achieved by first constructing an overcomplete graph capturing the vasculature, and then selecting and labeling the subset of edges that most likely represents the true vasculature. We formulate the latter problem as an Integer Program (IP), which can be solved efficiently to provable optimality. We evaluate our approach on a publicly available dataset of 50 cerebral MRA images, and demonstrate that it compares favorably against state-of-the-art methods.}
}
@ARTICLE{tueretken2016,
  author={Türetken, Engin and Benmansour, Fethallah and Andres, Bjoern and Głowacki, Przemysław and Pfister, Hanspeter and Fua, Pascal},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Reconstructing Curvilinear Networks Using Path Classifiers and Integer Programming}, 
  year={2016},
  volume={38},
  number={12},
  pages={2515-2530},
  doi={10.1109/TPAMI.2016.2519025}
}
@InProceedings{robben2014,
author="Robben, David
and T{\"u}retken, Engin
and Sunaert, Stefan
and Thijs, Vincent
and Wilms, Guy
and Fua, Pascal
and Maes, Frederik
and Suetens, Paul",
editor="Golland, Polina
and Hata, Nobuhiko
and Barillot, Christian
and Hornegger, Joachim
and Howe, Robert",
title="Simultaneous Segmentation and Anatomical Labeling of the Cerebral Vasculature",
booktitle="Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2014",
year="2014",
publisher="Springer International Publishing",
address="Cham",
pages="307--314",
abstract="We present a novel algorithm for the simultaneous segmentation and anatomical labeling of the cerebral vasculature. The method first constructs an overcomplete graph capturing the vasculature. It then selects and labels the subset of edges that most likely represents the true vasculature. Unlike existing approaches that first attempt to obtain a good segmentation and then perform labeling, we jointly optimize for both by simultaneously taking into account the image evidence and the prior knowledge about the geometry and connectivity of the vasculature. This results in an Integer Program (IP), which we solve optimally using a branch-and-cut algorithm. We evaluate our approach on a public dataset of 50 cerebral MRA images, and demonstrate that it compares favorably against state-of-the-art methods.",
isbn="978-3-319-10404-1"
}
@Article{tueretken2011,
author={T{\"u}retken, Engin
and Gonz{\'a}lez, Germ{\'a}n
and Blum, Christian
and Fua, Pascal},
title={Automated Reconstruction of Dendritic and Axonal Trees by Global Optimization with Geometric Priors},
journal={Neuroinformatics},
year={2011},
month={Sep},
day={01},
volume={9},
number={2},
pages={279-302},
abstract={We present a novel probabilistic approach to fully automated delineation of tree structures in noisy 2D images and 3D image stacks. Unlike earlier methods that rely mostly on local evidence, ours builds a set of candidate trees over many different subsets of points likely to belong to the optimal tree and then chooses the best one according to a global objective function that combines image evidence with geometric priors. Since the best tree does not necessarily span all the points, the algorithm is able to eliminate false detections while retaining the correct tree topology. Manually annotated brightfield micrographs, retinal scans and the DIADEM challenge datasets are used to evaluate the performance of our method. We used the DIADEM metric to quantitatively evaluate the topological accuracy of the reconstructions and showed that the use of the geometric regularization yields a substantial improvement.},
issn={1559-0089},
doi={10.1007/s12021-011-9122-1},
url={https://doi.org/10.1007/s12021-011-9122-1}
}
@InProceedings{tueretken2010,
author="T{\"u}retken, Engin
and Blum, Christian
and Gonz{\'a}lez, Germ{\'a}n
and Fua, Pascal",
editor="Jiang, Tianzi
and Navab, Nassir
and Pluim, Josien P. W.
and Viergever, Max A.",
title="Reconstructing Geometrically Consistent Tree Structures from Noisy Images",
booktitle="Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2010",
year="2010",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="291--299",
abstract="We present a novel approach to fully automated reconstruction of tree structures in noisy 2D images. Unlike in earlier approaches, we explicitly handle crossovers and bifurcation points, and impose geometric constraints while optimizing a global cost function. We use manually annotated retinal scans to evaluate our method and demonstrate that it brings about a very substantial improvement.",
isbn="978-3-642-15705-9"
}
@Article{todorov2020,
author={Todorov, Mihail Ivilinov
and Paetzold, Johannes Christian
and Schoppe, Oliver
and Tetteh, Giles
and Shit, Suprosanna
and Efremov, Velizar
and Todorov-V{\"o}lgyi, Katalin
and D{\"u}ring, Marco
and Dichgans, Martin
and Piraud, Marie
and Menze, Bjoern
and Ert{\"u}rk, Ali},
title={Machine learning analysis of whole mouse brain vasculature},
journal={Nature Methods},
year={2020},
month={Apr},
day={01},
volume={17},
number={4},
pages={442-449},
abstract={Tissue clearing methods enable the imaging of biological specimens without sectioning. However, reliable and scalable analysis of large imaging datasets in three dimensions remains a challenge. Here we developed a deep learning-based framework to quantify and analyze brain vasculature, named Vessel Segmentation {\&} Analysis Pipeline (VesSAP). Our pipeline uses a convolutional neural network (CNN) with a transfer learning approach for segmentation and achieves human-level accuracy. By using VesSAP, we analyzed the vascular features of whole C57BL/6J, CD1 and BALB/c mouse brains at the micrometer scale after registering them to the Allen mouse brain atlas. We report evidence of secondary intracranial collateral vascularization in CD1 mice and find reduced vascularization of the brainstem in comparison to the cerebrum. Thus, VesSAP enables unbiased and scalable quantifications of the angioarchitecture of cleared mouse brains and yields biological insights into the vascular function of the brain.},
issn={1548-7105},
doi={10.1038/s41592-020-0792-1},
url={https://doi.org/10.1038/s41592-020-0792-1}
}
@article {catmaid2016,
article_type = {journal},
title = {Quantitative neuroanatomy for connectomics in \textit{Drosophila}},
author = {Schneider-Mizell, Casey M and Gerhard, Stephan and Longair, Mark and Kazimiers, Tom and Li, Feng and Zwart, Maarten F and Champion, Andrew and Midgley, Frank M and Fetter, Richard D and Saalfeld, Stephan and Cardona, Albert},
editor = {Calabrese, Ronald L},
volume = 5,
year = 2016,
month = {mar},
pub_date = {2016-03-18},
pages = {e12059},
citation = {eLife 2016;5:e12059},
doi = {10.7554/eLife.12059},
url = {https://doi.org/10.7554/eLife.12059},
abstract = {Neuronal circuit mapping using electron microscopy demands laborious proofreading or reconciliation of multiple independent reconstructions. Here, we describe new methods to apply quantitative arbor and network context to iteratively proofread and reconstruct circuits and create anatomically enriched wiring diagrams. We measured the morphological underpinnings of connectivity in new and existing reconstructions of \textit{Drosophila} sensorimotor (larva) and visual (adult) systems. Synaptic inputs were preferentially located on numerous small, microtubule-free 'twigs' which branch off a single microtubule-containing 'backbone'. Omission of individual twigs accounted for 96\% of errors. However, the synapses of highly connected neurons were distributed across multiple twigs. Thus, the robustness of a strong connection to detailed twig anatomy was associated with robustness to reconstruction error. By comparing iterative reconstruction to the consensus of multiple reconstructions, we show that our method overcomes the need for redundant effort through the discovery and application of relationships between cellular neuroanatomy and synaptic connectivity.},
keywords = {connectomics, neuroanatomy, proprioception},
journal = {eLife},
issn = {2050-084X},
publisher = {eLife Sciences Publications, Ltd},
}
@article{catmaid2009,
    author = {Saalfeld, Stephan and Cardona, Albert and Hartenstein, Volker and Tomančák, Pavel},
    title = {CATMAID: collaborative annotation toolkit for massive amounts of image data},
    journal = {Bioinformatics},
    volume = {25},
    number = {15},
    pages = {1984-1986},
    year = {2009},
    month = {04},
    abstract = {Summary: High-resolution, three-dimensional (3D) imaging of large biological specimens generates massive image datasets that are difficult to navigate, annotate and share effectively. Inspired by online mapping applications like GoogleMaps™, we developed a decentralized web interface that allows seamless navigation of arbitrarily large image stacks. Our interface provides means for online, collaborative annotation of the biological image data and seamless sharing of regions of interest by bookmarking. The CATMAID interface enables synchronized navigation through multiple registered datasets even at vastly different scales such as in comparisons between optical and electron microscopy.Availability:  http://fly.mpi-cbg.de/catmaidContact: tomancak@mpi-cbg.de},
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btp266},
    url = {https://doi.org/10.1093/bioinformatics/btp266},
    eprint = {https://academic.oup.com/bioinformatics/article-pdf/25/15/1984/48994215/bioinformatics\_25\_15\_1984.pdf},
}
@Article{Drees2021voreenSkel,
author={Drees, Dominik
and Scherzinger, Aaron
and H{\"a}gerling, Ren{\'e}
and Kiefer, Friedemann
and Jiang, Xiaoyi},
title={Scalable robust graph and feature extraction for arbitrary vessel networks in large volumetric datasets},
journal={BMC Bioinformatics},
year={2021},
month={Jun},
day={26},
volume={22},
number={1},
pages={346},
abstract={Recent advances in 3D imaging technologies provide novel insights to researchers and reveal finer and more detail of examined specimen, especially in the biomedical domain, but also impose huge challenges regarding scalability for automated analysis algorithms due to rapidly increasing dataset sizes. In particular, existing research towards automated vessel network analysis does not always consider memory requirements of proposed algorithms and often generates a large number of spurious branches for structures consisting of many voxels. Additionally, very often these algorithms have further restrictions such as the limitation to tree topologies or relying on the properties of specific image modalities.},
issn={1471-2105},
doi={10.1186/s12859-021-04262-w},
url={https://doi.org/10.1186/s12859-021-04262-w}
}
@ARTICLE{Drees2019gerome,
  author={Drees, Dominik and Scherzinger, Aaron and Jiang, Xiaoyi},
  journal={IEEE Access}, 
  title={GERoMe-a Method for Evaluating Stability of Graph Extraction Algorithms Without Ground Truth}, 
  year={2019},
  volume={7},
  number={},
  pages={21744-21755},
  keywords={Image segmentation;Data mining;Robustness;Stability criteria;Image edge detection;Manuals;Evaluation;graph extraction;robustness;stability},
  doi={10.1109/ACCESS.2019.2898754}
}

@article{xie2025,
title = {Efficient anatomical labeling of pulmonary tree structures via deep point-graph representation-based implicit fields},
journal = {Medical Image Analysis},
volume = {99},
pages = {103367},
year = {2025},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2024.103367},
url = {https://www.sciencedirect.com/science/article/pii/S1361841524002925},
author = {Kangxian Xie and Jiancheng Yang and Donglai Wei and Ziqiao Weng and Pascal Fua},
keywords = {Pulmonary tree labeling, Graph, Point cloud, Implicit function, 3D deep learning},
abstract = {Pulmonary diseases rank prominently among the principal causes of death worldwide. Curing them will require, among other things, a better understanding of the complex 3D tree-shaped structures within the pulmonary system, such as airways, arteries, and veins. Traditional approaches using high-resolution image stacks and standard CNNs on dense voxel grids face challenges in computational efficiency, limited resolution, local context, and inadequate preservation of shape topology. Our method addresses these issues by shifting from dense voxel to sparse point representation, offering better memory efficiency and global context utilization. However, the inherent sparsity in point representation can lead to a loss of crucial connectivity in tree-shaped structures. To mitigate this, we introduce graph learning on skeletonized structures, incorporating differentiable feature fusion for improved topology and long-distance context capture. Furthermore, we employ an implicit function for efficient conversion of sparse representations into dense reconstructions end-to-end. The proposed method not only delivers state-of-the-art performance in labeling accuracy, both overall and at key locations, but also enables efficient inference and the generation of closed surface shapes. Addressing data scarcity in this field, we have also curated a comprehensive dataset to validate our approach. Data and code are available at https://github.com/M3DV/pulmonary-tree-labeling.}
}

@article{kuhn1955hungarian,
  author    = {Harold W. Kuhn},
  title     = {The Hungarian Method for the Assignment Problem},
  journal   = {Naval Research Logistics Quarterly},
  volume    = {2},
  number    = {1-2},
  pages     = {83--97},
  year      = {1955},
  publisher = {Wiley},
  doi       = {10.1002/nav.3800020109}
}

@book{edelsbrunner2010computational_topology,
author = {Edelsbrunner, Herbert and Harer, John},
year = {2010},
month = {01},
pages = {},
title = {Computational Topology: An Introduction},
isbn = {978-0-8218-4925-5},
doi = {10.1007/978-3-540-33259-6_7}
}

@misc{peyré2020computationaloptimaltransport,
      title={Computational Optimal Transport}, 
      author={Gabriel Peyré and Marco Cuturi},
      year={2020},
      eprint={1803.00567},
      archivePrefix={arXiv},
      primaryClass={stat.ML},
      url={https://arxiv.org/abs/1803.00567}, 
}
@Article{Pampols-Perez2025piezo2,
author={Pampols-Perez, Mireia
and F{\"u}rst, Carina
and S{\'a}nchez-Carranza, Oscar
and Cano, Elena
and Garcia-Contreras, Jonathan Alexis
and Mais, Lisa
and Luo, Wenhan
and Raimundo, Sandra
and Lindberg, Eric L.
and Taube, Martin
and Heuser, Arnd
and Sporbert, Anje
and Kainmueller, Dagmar
and Bernabeu, Miguel O.
and H{\"u}bner, Norbert
and Gerhardt, Holger
and Lewin, Gary R.
and Hammes, Annette},
title={Mechanosensitive PIEZO2 channels shape coronary artery development},
journal={Nature Cardiovascular Research},
year={2025},
month={Jul},
day={01},
volume={4},
number={7},
pages={921-937},
abstract={Coronary arteries develop under constant mechanical stress. However, the role of mechanosensitive ion channels in this process remains poorly understood. Here we show that the ion channel PIEZO2, which responds to mechanical stimuli, is expressed in specific coronary endothelial cell populations during a critical phase of coronary vasculature remodeling. These Piezo2+ coronary endothelial cells show distinct transcriptional profiles and have mechanically activated ionic currents. Strikingly, PIEZO2 loss-of-function mouse embryos and mice with human pathogenic variants of PIEZO2 show abnormal coronary vessel development and cardiac left ventricular hyperplasia. We conclude that an optimal balance of PIEZO2 channel function contributes to proper coronary vessel formation, structural integrity and remodeling, and is likely to support normal cardiac function. Our study highlights the importance of mechanical cues in cardiovascular development and suggests that defects in this mechanosensing pathway may contribute to congenital heart conditions.},
issn={2731-0590},
doi={10.1038/s44161-025-00677-3},
url={https://doi.org/10.1038/s44161-025-00677-3}
}
@Article{Obenaus2017,
author={Obenaus, Andre
and Ng, Michelle
and Orantes, Amanda M.
and Kinney-Lang, Eli
and Rashid, Faisal
and Hamer, Mary
and DeFazio, Richard A.
and Tang, Jiping
and Zhang, John H.
and Pearce, William J.},
title={Traumatic brain injury results in acute rarefication of the vascular network},
journal={Scientific Reports},
year={2017},
month={Mar},
day={22},
volume={7},
number={1},
pages={239},
abstract={The role of the cerebrovascular network and its acute response to TBI is poorly defined and emerging evidence suggests that cerebrovascular reactivity is altered. We explored how cortical vessels are physically altered following TBI using a newly developed technique, vessel painting. We tested our hypothesis that a focal moderate TBI results in global decrements to structural aspects of the vasculature. Rats (na{\"i}ve, sham-operated, TBI) underwent a moderate controlled cortical impact. Animals underwent vessel painting perfusion to label the entire cortex at 1 day post TBI followed by whole brain axial and coronal images using a wide-field fluorescence microscope. Cortical vessel network characteristics were analyzed for classical angiographic features (junctions, lengths) wherein we observed significant global (both hemispheres) reductions in vessel junctions and vessel lengths of 33{\%} and 22{\%}, respectively. Biological complexity can be quantified using fractal geometric features where we observed that fractal measures were also reduced significantly by 33{\%}, 16{\%} and 13{\%} for kurtosis, peak value frequency and skewness, respectively. Acutely after TBI there is a reduction in vascular network and vascular complexity that are exacerbated at the lesion site and provide structural evidence for the bilateral hemodynamic alterations that have been reported in patients after TBI.},
issn={2045-2322},
doi={10.1038/s41598-017-00161-4},
url={https://doi.org/10.1038/s41598-017-00161-4}
}
@article {cheng2024,
article_type = {journal},
title = {Hemodynamics regulate spatiotemporal artery muscularization in the developing circle of Willis},
author = {Cheng, Siyuan and Xia, Ivan Fan and Wanner, Renate and Abello, Javier and Stratman, Amber N and Nicoli, Stefania},
editor = {Childs, Sarah and Stainier, Didier YR},
volume = 13,
year = 2024,
month = {jul},
pub_date = {2024-07-10},
pages = {RP94094},
citation = {eLife 2024;13:RP94094},
doi = {10.7554/eLife.94094},
url = {https://doi.org/10.7554/eLife.94094},
abstract = {Vascular smooth muscle cells (VSMCs) envelop vertebrate brain arteries and play a crucial role in regulating cerebral blood flow and neurovascular coupling. The dedifferentiation of VSMCs is implicated in cerebrovascular disease and neurodegeneration. Despite its importance, the process of VSMC differentiation on brain arteries during development remains inadequately characterized. Understanding this process could aid in reprogramming and regenerating dedifferentiated VSMCs in cerebrovascular diseases. In this study, we investigated VSMC differentiation on zebrafish circle of Willis (CoW), comprising major arteries that supply blood to the vertebrate brain. We observed that arterial specification of CoW endothelial cells (ECs) occurs after their migration from cranial venous plexus to form CoW arteries. Subsequently, \textit{acta2}+ VSMCs differentiate from \textit{pdgfrb}+ mural cell progenitors after they were recruited to CoW arteries. The progression of VSMC differentiation exhibits a spatiotemporal pattern, advancing from anterior to posterior CoW arteries. Analysis of blood flow suggests that earlier VSMC differentiation in anterior CoW arteries correlates with higher red blood cell velocity and wall shear stress. Furthermore, pulsatile flow induces differentiation of human brain PDGFRB+ mural cells into VSMCs, and blood flow is required for VSMC differentiation on zebrafish CoW arteries. Consistently, flow-responsive transcription factor \textit{klf2a} is activated in ECs of CoW arteries prior to VSMC differentiation, and \textit{klf2a} knockdown delays VSMC differentiation on anterior CoW arteries. In summary, our findings highlight blood flow activation of endothelial \textit{klf2a} as a mechanism regulating initial VSMC differentiation on vertebrate brain arteries.},
keywords = {flow hemodynamics, artery muscularization, brain artery development, vascular smooth muscle cell differentiation, circle of Willis},
journal = {eLife},
issn = {2050-084X},
publisher = {eLife Sciences Publications, Ltd},
}
@article {soubeyrand2023,
article_type = {journal},
title = {Three-dimensional imaging of vascular development in the mouse epididymis},
author = {Damon-Soubeyrand, Christelle and Bongiovanni, Antonino and Chorfa, Areski and Goubely, Chantal and Pirot, Nelly and Pardanaud, Luc and Piboin-Fragner, Laurence and Vachias, Caroline and Bravard, Stephanie and Guiton, Rachel and Thomas, Jean-Leon and Saez, Fabrice and Kocer, Ayhan and Tardivel, Meryem and Drevet, Joël R and Henry-Berger, Joelle},
editor = {Kumar, T Rajendra and Zaidi, Mone},
volume = 12,
year = 2023,
month = {jun},
pub_date = {2023-06-13},
pages = {e82748},
citation = {eLife 2023;12:e82748},
doi = {10.7554/eLife.82748},
url = {https://doi.org/10.7554/eLife.82748},
abstract = {Long considered an accessory tubule of the male reproductive system, the epididymis is proving to be a key determinant of male fertility. In addition to its secretory role in ensuring functional maturation and survival of spermatozoa, the epididymis has a complex immune function. Indeed, it must manage both peripheral tolerance to sperm antigens foreign to the immune system and the protection of spermatozoa as well as the organ itself against pathogens ascending the epididymal tubule. Although our knowledge of the immunobiology of this organ is beginning to accumulate at the molecular and cellular levels, the organization of blood and lymphatic networks of this tissue, important players in the immune response, remains largely unknown. In the present report, we have taken advantage of a VEGFR3:YFP transgenic mouse model. Using high-resolution three-dimensional (3D) imaging and organ clearing coupled with multiplex immunodetections of lymphatic (LYVE1, PDPN, PROX1) and/or blood (PLVAP/Meca32) markers, we provide a simultaneous deep 3D view of the lymphatic and blood epididymal vasculature in the mature adult mouse as well as during postnatal development.},
keywords = {epididymis, lymphatics, spermatozoa},
journal = {eLife},
issn = {2050-084X},
publisher = {eLife Sciences Publications, Ltd},
}
@Article{Walek2023,
author={Walek, Konrad W.
and Stefan, Sabina
and Lee, Jang-Hoon
and Puttigampala, Pooja
and Kim, Anna H.
and Park, Seong Wook
and Marchand, Paul J.
and Lesage, Frederic
and Liu, Tao
and Huang, Yu-Wen Alvin
and Boas, David A.
and Moore, Christopher
and Lee, Jonghwan},
title={Near-lifespan longitudinal tracking of brain microvascular morphology, topology, and flow in male mice},
journal={Nature Communications},
year={2023},
month={May},
day={24},
volume={14},
number={1},
pages={2982},
abstract={In age-related neurodegenerative diseases, pathology often develops slowly across the lifespan. As one example, in diseases such as Alzheimer's, vascular decline is believed to onset decades ahead of symptomology. However, challenges inherent in current microscopic methods make longitudinal tracking of such vascular decline difficult. Here, we describe a suite of methods for measuring brain vascular dynamics and anatomy in mice for over seven months in the same field of view. This approach is enabled by advances in optical coherence tomography (OCT) and image processing algorithms including deep learning. These integrated methods enabled us to simultaneously monitor distinct vascular properties spanning morphology, topology, and function of the microvasculature across all scales: large pial vessels, penetrating cortical vessels, and capillaries. We have demonstrated this technical capability in wild-type and 3xTg male mice. The capability will allow comprehensive and longitudinal study of a broad range of progressive vascular diseases, and normal aging, in key model systems.},
issn={2041-1723},
doi={10.1038/s41467-023-38609-z},
url={https://doi.org/10.1038/s41467-023-38609-z}
}
@InProceedings{Wittmann_2025_CVPR,
    author    = {Wittmann, Bastian and Wattenberg, Yannick and Amiranashvili, Tamaz and Shit, Suprosanna and Menze, Bjoern},
    title     = {vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20874-20884}
}
@article{Bumgarner2022vesselvio,
title = {Open-source analysis and visualization of segmented vasculature datasets with VesselVio},
journal = {Cell Reports Methods},
volume = {2},
number = {4},
pages = {100189},
year = {2022},
issn = {2667-2375},
doi = {https://doi.org/10.1016/j.crmeth.2022.100189},
url = {https://www.sciencedirect.com/science/article/pii/S2667237522000443},
author = {Jacob R. Bumgarner and Randy J. Nelson},
keywords = {vasculature, vasculature analysis, vasculature visualization, cerebrovasculature, open-source application, Python, vascular networks},
abstract = {Summary
Vascular networks are fundamental components of biological systems. Quantitative analysis and observation of the features of these networks can improve our understanding of their roles in health and disease. Recent advancements in imaging technologies have enabled the generation of large-scale vasculature datasets, but barriers to analyzing these datasets remain. Modern analysis options are mainly limited to paid applications or open-source terminal-based software that requires programming knowledge with high learning curves. Here, we describe VesselVio, an open-source application developed to analyze and visualize pre-binarized vasculature datasets and pre-constructed vascular graphs. Vasculature datasets and graphs can be loaded with annotations and processed with custom parameters. Here, the program is tested on ground-truth datasets and is compared with current pipelines. The utility of VesselVio is demonstrated by the analysis of multiple formats of 2D and 3D datasets acquired with several imaging modalities, including annotated mouse whole-brain vasculature volumes.}
}
@inproceedings{paetzold2021vesselgraph,
 author = {Paetzold, Johannes C. and McGinnis, Julian and Shit, Suprosanna and Ezhov, Ivan and B\"{u}schl, Paul and Prabhakar, Chinmay and Sekuboyina, Anjany and Todorov, Mihail and Kaissis, Georgios and Ert\"{u}rk, Ali and G\"{u}nnemann, Stephan and Menze, Bjoern},
 booktitle = {Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks},
 editor = {J. Vanschoren and S. Yeung},
 pages = {},
 title = {Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience},
 url = {https://datasets-benchmarks-proceedings.neurips.cc/paper_files/paper/2021/file/c9f0f895fb98ab9159f51fd0297e236d-Paper-round2.pdf},
 volume = {1},
 year = {2021}
}
@INPROCEEDINGS{wittmann2024link,
  author={Wittmann, Bastian and Paetzold, Johannes C. and Prabhakar, Chinmay and Rueckert, Daniel and Menze, Bjoern},
  booktitle={2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, 
  title={Link Prediction for Flow-Driven Spatial Networks}, 
  year={2024},
  volume={},
  number={},
  pages={2460-2469},
  keywords={Measurement;Computer vision;Codes;Roads;Transportation;Benchmark testing;Prediction algorithms;Algorithms;Machine learning architectures;formulations;and algorithms;Applications;Biomedical / healthcare / medicine},
  doi={10.1109/WACV57701.2024.00246}}
@inproceedings{
zhu2021deformable,
title={Deformable {\{}DETR{\}}: Deformable Transformers for End-to-End Object Detection},
author={Xizhou Zhu and Weijie Su and Lewei Lu and Bin Li and Xiaogang Wang and Jifeng Dai},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=gZ9hCDWe6ke}
}
@InProceedings{carion2020eccv,
author="Carion, Nicolas
and Massa, Francisco
and Synnaeve, Gabriel
and Usunier, Nicolas
and Kirillov, Alexander
and Zagoruyko, Sergey",
editor="Vedaldi, Andrea
and Bischof, Horst
and Brox, Thomas
and Frahm, Jan-Michael",
title="End-to-End Object Detection with Transformers",
booktitle="Computer Vision -- ECCV 2020",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="213--229",
abstract="We present a new method that views object detection as a direct set prediction problem. Our approach streamlines the detection pipeline, effectively removing the need for many hand-designed components like a non-maximum suppression procedure or anchor generation that explicitly encode our prior knowledge about the task. The main ingredients of the new framework, called DEtection TRansformer or DETR, are a set-based global loss that forces unique predictions via bipartite matching, and a transformer encoder-decoder architecture. Given a fixed small set of learned object queries, DETR reasons about the relations of the objects and the global image context to directly output the final set of predictions in parallel. The new model is conceptually simple and does not require a specialized library, unlike many other modern detectors. DETR demonstrates accuracy and run-time performance on par with the well-established and highly-optimized Faster R-CNN baseline on the challenging COCO object detection dataset. Moreover, DETR can be easily generalized to produce panoptic segmentation in a unified manner. We show that it significantly outperforms competitive baselines. Training code and pretrained models are available at https://github.com/facebookresearch/detr.",
isbn="978-3-030-58452-8"
}
@Article{Lyu2022reta,
author={Lyu, Xingzheng
and Cheng, Li
and Zhang, Sanyuan},
title={The RETA Benchmark for Retinal Vascular Tree Analysis},
journal={Scientific Data},
year={2022},
month={Jul},
day={11},
volume={9},
number={1},
pages={397},
abstract={Topological and geometrical analysis of retinal blood vessels could be a cost-effective way to detect various common diseases. Automated vessel segmentation and vascular tree analysis models require powerful generalization capability in clinical applications. In this work, we constructed a novel benchmark RETA with 81 labelled vessel masks aiming to facilitate retinal vessel analysis. A semi-automated coarse-to-fine workflow was proposed for vessel annotation task. During database construction, we strived to control inter-annotator and intra-annotator variability by means of multi-stage annotation and label disambiguation on self-developed dedicated software. In addition to binary vessel masks, we obtained other types of annotations including artery/vein masks, vascular skeletons, bifurcations, trees and abnormalities. Subjective and objective quality validations of the annotated vessel masks demonstrated significantly improved quality over the existing open datasets. Our annotation software is also made publicly available serving the purpose of pixel-level vessel visualization. Researchers could develop vessel segmentation algorithms and evaluate segmentation performance using RETA. Moreover, it might promote the study of cross-modality tubular structure segmentation and analysis.},
issn={2052-4463},
doi={10.1038/s41597-022-01507-y},
url={https://doi.org/10.1038/s41597-022-01507-y}
}
@InProceedings{Shit2021cldice,
    author    = {Shit, Suprosanna and Paetzold, Johannes C. and Sekuboyina, Anjany and Ezhov, Ivan and Unger, Alexander and Zhylka, Andrey and Pluim, Josien P. W. and Bauer, Ulrich and Menze, Bjoern H.},
    title     = {clDice - A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {16560-16569}
}
@InProceedings{lin2014coco,
author="Lin, Tsung-Yi
and Maire, Michael
and Belongie, Serge
and Hays, James
and Perona, Pietro
and Ramanan, Deva
and Doll{\'a}r, Piotr
and Zitnick, C. Lawrence",
editor="Fleet, David
and Pajdla, Tomas
and Schiele, Bernt
and Tuytelaars, Tinne",
title="Microsoft COCO: Common Objects in Context",
booktitle="Computer Vision -- ECCV 2014",
year="2014",
publisher="Springer International Publishing",
address="Cham",
pages="740--755",
abstract="We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.",
isbn="978-3-319-10602-1"
}
@Article{Foucart2023,
author={Foucart, Adrien
and Debeir, Olivier
and Decaestecker, Christine},
title={Panoptic quality should be avoided as a metric for assessing cell nuclei segmentation and classification in digital pathology},
journal={Scientific Reports},
year={2023},
month={May},
day={27},
volume={13},
number={1},
pages={8614},
abstract={Panoptic Quality (PQ), designed for the task of ``Panoptic Segmentation'' (PS), has been used in several digital pathology challenges and publications on cell nucleus instance segmentation and classification (ISC) since its introduction in 2019. Its purpose is to encompass the detection and the segmentation aspects of the task in a single measure, so that algorithms can be ranked according to their overall performance. A careful analysis of the properties of the metric, its application to ISC and the characteristics of nucleus ISC datasets, shows that is not suitable for this purpose and should be avoided. Through a theoretical analysis we demonstrate that PS and ISC, despite their similarities, have some fundamental differences that make PQ unsuitable. We also show that the use of the Intersection over Union as a matching rule and as a segmentation quality measure within PQ is not adapted for such small objects as nuclei. We illustrate these findings with examples taken from the NuCLS and MoNuSAC datasets. The code for replicating our results is available on GitHub (https://github.com/adfoucart/panoptic-quality-suppl).},
issn={2045-2322},
doi={10.1038/s41598-023-35605-7},
url={https://doi.org/10.1038/s41598-023-35605-7}
}
@INPROCEEDINGS{li2023ted,
  author={Li, Wenjian and Wu, Guannan and Li, Huiqi},
  booktitle={2023 IEEE 18th Conference on Industrial Electronics and Applications (ICIEA)}, 
  title={Similarity evaluation of retinal vascular network based on tree edit distance}, 
  year={2023},
  volume={},
  number={},
  pages={722-727},
  keywords={Macular degeneration;Industrial electronics;Diabetic retinopathy;Costs;Image color analysis;Cost function;Retinal vessels;retinal vascular network similarity;retinal vessel tree;tree edit distance},
  doi={10.1109/ICIEA58696.2023.10241828}}

@article{matula2015tra,
    doi = {10.1371/journal.pone.0144959},
    author = {Matula, Pavel AND Maška, Martin AND Sorokin, Dmitry V. AND Matula, Petr AND Ortiz-de-Solórzano, Carlos AND Kozubek, Michal},
    journal = {PLOS ONE},
    publisher = {Public Library of Science},
    title = {Cell Tracking Accuracy Measurement Based on Comparison of Acyclic Oriented Graphs},
    year = {2015},
    month = {12},
    volume = {10},
    url = {https://doi.org/10.1371/journal.pone.0144959},
    pages = {1-19},
    abstract = {Tracking motile cells in time-lapse series is challenging and is required in many biomedical applications. Cell tracks can be mathematically represented as acyclic oriented graphs. Their vertices describe the spatio-temporal locations of individual cells, whereas the edges represent temporal relationships between them. Such a representation maintains the knowledge of all important cellular events within a captured field of view, such as migration, division, death, and transit through the field of view. The increasing number of cell tracking algorithms calls for comparison of their performance. However, the lack of a standardized cell tracking accuracy measure makes the comparison impracticable. This paper defines and evaluates an accuracy measure for objective and systematic benchmarking of cell tracking algorithms. The measure assumes the existence of a ground-truth reference, and assesses how difficult it is to transform a computed graph into the reference one. The difficulty is measured as a weighted sum of the lowest number of graph operations, such as split, delete, and add a vertex and delete, add, and alter the semantics of an edge, needed to make the graphs identical. The measure behavior is extensively analyzed based on the tracking results provided by the participants of the first Cell Tracking Challenge hosted by the 2013 IEEE International Symposium on Biomedical Imaging. We demonstrate the robustness and stability of the measure against small changes in the choice of weights for diverse cell tracking algorithms and fluorescence microscopy datasets. As the measure penalizes all possible errors in the tracking results and is easy to compute, it may especially help developers and analysts to tune their algorithms according to their needs.},
    number = {12},

}
@InProceedings{fisbe,
    author    = {Mais, Lisa and Hirsch, Peter and Managan, Claire and Kandarpa, Ramya and Rumberger, Josef Lorenz and Reinke, Annika and Maier-Hein, Lena and Ihrke, Gudrun and Kainmueller, Dagmar},
    title     = {FISBe: A Real-World Benchmark Dataset for Instance Segmentation of Long-Range Thin Filamentous Structures},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2024},
    pages     = {22249-22259}
}

@article{schneider2012tissue,
  title={Tissue metabolism driven arterial tree generation},
  author={Schneider, Matthias and Reichold, Johannes and Weber, Bruno and Sz{\'e}kely, G{\'a}bor and Hirsch, Sven},
  journal={Medical image analysis},
  volume={16},
  number={7},
  pages={1397--1414},
  year={2012},
  publisher={Elsevier}
}

@article{dijkstra1959note,
  title={A note on two problems in connexion with graphs},
  author={Dijkstra, Edsger W},
  journal={Numerische mathematik},
  volume={1},
  number={1},
  pages={269--271},
  year={1959},
  publisher={Springer}
}

@article{baeverified,
  title={Verified details},
  author={Bae, Alex and Collman, Forrest and Li, Peter}
}
@article{wu2022,
title = {Vessel-GAN: Angiographic reconstructions from myocardial CT perfusion with explainable generative adversarial networks},
journal = {Future Generation Computer Systems},
volume = {130},
pages = {128-139},
year = {2022},
issn = {0167-739X},
doi = {https://doi.org/10.1016/j.future.2021.12.007},
url = {https://www.sciencedirect.com/science/article/pii/S0167739X2100488X},
author = {Chulin Wu and Heye Zhang and Jiaqi Chen and Zhifan Gao and Pengfei Zhang and Khan Muhammad and Javier {Del Ser}},
keywords = {Myocardial CT perfusion, Angiography, Explainable AI, Generative adversarial networks, Medical image processing},
abstract = {Dynamic CT angiography derived from CT perfusion data can obviate a separate coronary CT angiography and the use of ionizing radiation and contrast agent, thereby enhancing patient safety. However, the image quality of dynamic CT angiography is inferior to standard CT angiography images in many studies. This paper proposes an explainable generative adversarial network named vessel-GAN, which resorts to explainable knowledge-based artificial intelligence to perform image translation with increased trustworthiness. Specifically, we design a loss term to better learn the representations of blood vessels in CT angiography images. The loss term based on expert knowledge guides the generator to focus its training on the important features predicted by the discriminator. Additionally, we propose a generator architecture that effectively fuses spatio-temporal representations and further enhances temporal consistency, thereby improving the quality of the generated CT angiography images. The experiment is conducted on a dataset consisting of 232 patients with suspected coronary artery stenosis. Experimental results show that the PSNR value of vessel-GAN is 28.32 dB, SSIM value is 0.91 and MAE value is 47.36. To validate the effectiveness of the proposed synthesis method, we compare that with other image translation frameworks and GAN-based methods. Compared to other image translation methods, the proposed method vessel-GAN can generate more clearly visible blood vessels from source perfusion images. The CTA images generated by vessel-GAN are closer to the real CTA due to the use of adversarial learning. Compared with other GAN-based methods, vessel-GAN can produce sharper and more homogeneous outputs, including realistic vascular structures. The experiment demonstrates that the explainable generative adversarial network has superior performance for it can better control how models learn. Overall, the CT angiography images generated by vessel-GAN can potentially replace a separate standard CT angiography, allowing the possibility of “one-stop” cardiac examination for high-risk coronary artery disease patients who need assessment of myocardial ischemia.}
}

@software{Silversmith_Kimimaro_Skeletonize_densely_2021,
author = {Silversmith, William and Bae, J. Alexander and Li, Peter H. and Wilson, A.M.},
doi = {10.5281/zenodo.5539913},
month = sep,
title = {{Kimimaro: Skeletonize densely labeled 3D image segmentations}},
version = {3.0.0},
year = {2021}
}

@article{liu2021assessment,
  title={Assessment of Vascular Network Connectivity of Hepatocellular Carcinoma Using Graph-Based Approach},
  author={Liu, Qiaoyu and Zhang, Boyu and Wang, Luna and Zheng, Rencheng and Qiang, Jinwei and Wang, He and Yan, Fuhua and Li, Ruokun},
  journal={Frontiers in Oncology},
  volume={11},
  pages={668874},
  year={2021}
}

@article{kraeker2023abstract,
  title={Abstract P203: Microvascular Imaging As Early Predictor For Cardiac Dysfunction After Preeclampsia},
  author={Kraeker, Kristin and Haase, Nadine and Kedziora, Sarah and Frolova, Alina and Schuette, Till and Herse, Florian and Mueller, Dominik N and Dechend, Ralf},
  journal={Hypertension},
  volume={80},
  number={Suppl\_1},
  pages={AP203--AP203},
  year={2023},
  publisher={Lippincott Williams \& Wilkins Hagerstown, MD}
}
@Article{Malin-Mayor2023,
author={Malin-Mayor, Caroline
and Hirsch, Peter
and Guignard, Leo
and McDole, Katie
and Wan, Yinan
and Lemon, William C.
and Kainmueller, Dagmar
and Keller, Philipp J.
and Preibisch, Stephan
and Funke, Jan},
title={Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations},
journal={Nature Biotechnology},
year={2023},
month={Jan},
day={01},
volume={41},
number={1},
pages={44-49},
abstract={We present a method to automatically identify and track nuclei in time-lapse microscopy recordings of entire developing embryos. The method combines deep learning and global optimization. On a mouse dataset, it reconstructs 75.8{\%} of cell lineages spanning 1 h, as compared to 31.8{\%} for the competing method. Our approach improves understanding of where and when cell fate decisions are made in developing embryos, tissues, and organs.},
issn={1546-1696},
doi={10.1038/s41587-022-01427-7},
url={https://doi.org/10.1038/s41587-022-01427-7}
}
@InProceedings{patchperpix,
author="Mais, Lisa
and Hirsch, Peter
and Kainmueller, Dagmar",
editor="Vedaldi, Andrea
and Bischof, Horst
and Brox, Thomas
and Frahm, Jan-Michael",
title="PatchPerPix for Instance Segmentation",
booktitle="Computer Vision -- ECCV 2020",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="288--304",
abstract="We present a novel method for proposal free instance segmentation that can handle sophisticated object shapes which span large parts of an image and form dense object clusters with crossovers. Our method is based on predicting dense local shape descriptors, which we assemble to form instances. All instances are assembled simultaneously in one go. To our knowledge, our method is the first non-iterative method that yields instances that are composed of learnt shape patches. We evaluate our method on a diverse range of data domains, where it defines the new state of the art on four benchmarks, namely the ISBI 2012 EM segmentation benchmark, the BBBC010 C. elegans dataset, and 2d as well as 3d fluorescence microscopy data of cell nuclei. We show furthermore that our method also applies to 3d light microscopy data of Drosophila neurons, which exhibit extreme cases of complex shape clusters.",
isbn="978-3-030-58595-2"
}
@inproceedings{
lux2025topograph,
title={Topograph: An Efficient Graph-Based Framework for Strictly Topology Preserving Image Segmentation},
author={Laurin Lux and Alexander H Berger and Alexander Weers and Nico Stucki and Daniel Rueckert and Ulrich Bauer and Johannes C. Paetzold},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=Q0zmmNNePz}
}
@InProceedings{kirchhoff2024,
author="Kirchhoff, Yannick
and Rokuss, Maximilian R.
and Roy, Saikat
and Kovacs, Balint
and Ulrich, Constantin
and Wald, Tassilo
and Zenk, Maximilian
and Vollmuth, Philipp
and Kleesiek, Jens
and Isensee, Fabian
and Maier-Hein, Klaus",
editor="Leonardis, Ale{\v{s}}
and Ricci, Elisa
and Roth, Stefan
and Russakovsky, Olga
and Sattler, Torsten
and Varol, G{\"u}l",
title="Skeleton Recall Loss for Connectivity Conserving and Resource Efficient Segmentation of Thin Tubular Structures",
booktitle="Computer Vision -- ECCV 2024",
year="2024",
publisher="Springer Nature Switzerland",
address="Cham",
pages="218--234",
abstract="Accurately segmenting thin tubular structures, such as vessels, nerves, roads or concrete cracks, is a crucial task in computer vision. Standard deep learning-based segmentation loss functions, such as Dice or Cross-Entropy, focus on volumetric overlap, often at the expense of preserving structural connectivity or topology. This can lead to segmentation errors that adversely affect downstream tasks, including flow calculation, navigation, and structural inspection. Although current topology-focused losses mark an improvement, they introduce significant computational and memory overheads. This is particularly relevant for 3D data, rendering these losses infeasible for larger volumes as well as increasingly important multi-class segmentation problems. To mitigate this, we propose a novel Skeleton Recall Loss, which effectively addresses these challenges by circumventing intensive GPU-based calculations with inexpensive CPU operations. It demonstrates overall superior performance to current state-of-the-art approaches on five public datasets for topology-preserving segmentation, while substantially reducing computational overheads by more than {\$}{\$}90{\backslash}{\%}{\$}{\$}90{\%}. In doing so, we introduce the first multi-class capable loss function for thin structure segmentation, excelling in both efficiency and efficacy for topology-preservation. Our code is available to the community, providing a foundation for further advancements, at: https://github.com/MIC-DKFZ/Skeleton-Recall.",
isbn="978-3-031-72980-5"
}

@Article{RiosCoronado2025,
author={Rios Coronado, Pamela E.
and Zhou, Jiayan
and Fan, Xiaochen
and Zanetti, Daniela
and Naftaly, Jeffrey A.
and Prabala, Pratima
and Mart{\'i}nez Jaimes, Azalia M.
and Farah, Elie N.
and Kundu, Soumya
and Deshpande, Salil S.
and Evergreen, Ivy
and Kho, Pik Fang
and Ma, Qixuan
and Hilliard, Austin T.
and Abramowitz, Sarah
and Pyarajan, Saiju
and Dochtermann, Daniel
and Damrauer, Scott M.
and Chang, Kyong-Mi
and Levin, Michael G.
and Winn, Virginia D.
and Pa{\c{s}}ca, Anca M.
and Plomondon, Mary E.
and Waldo, Stephen W.
and Tsao, Philip S.
and Kundaje, Anshul
and Chi, Neil C.
and Clarke, Shoa L.
and Red-Horse, Kristy
and Assimes, Themistocles L.},
title={<em>CXCL12</em> drives natural variation in coronary artery anatomy across diverse populations},
journal={Cell},
year={2025},
month={Apr},
day={03},
publisher={Elsevier},
volume={188},
number={7},
pages={1784-1806.e22},
issn={0092-8674},
doi={10.1016/j.cell.2025.02.005},
url={https://doi.org/10.1016/j.cell.2025.02.005}
}
@Article{Sheridan2023lsd,
author={Sheridan, Arlo
and Nguyen, Tri M.
and Deb, Diptodip
and Lee, Wei-Chung Allen
and Saalfeld, Stephan
and Turaga, Srinivas C.
and Manor, Uri
and Funke, Jan},
title={Local shape descriptors for neuron segmentation},
journal={Nature Methods},
year={2023},
month={Feb},
day={01},
volume={20},
number={2},
pages={295-303},
abstract={We present an auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of local shape descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor affinities for neuron boundary detection. The shape descriptors capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a study comparing several existing methods across various specimen, imaging techniques, and resolutions, auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinity-based segmentation methods to be on par with the current state of the art for neuron segmentation (flood-filling networks), while being two orders of magnitudes more efficient---a critical requirement for the processing of future petabyte-sized datasets.},
issn={1548-7105},
doi={10.1038/s41592-022-01711-z},
url={https://doi.org/10.1038/s41592-022-01711-z}
}
@article{nblast2016,
title = {NBLAST: Rapid, Sensitive Comparison of Neuronal Structure and Construction of Neuron Family Databases},
journal = {Neuron},
volume = {91},
number = {2},
pages = {293-311},
year = {2016},
issn = {0896-6273},
doi = {https://doi.org/10.1016/j.neuron.2016.06.012},
url = {https://www.sciencedirect.com/science/article/pii/S0896627316302653},
author = {Marta Costa and James D. Manton and Aaron D. Ostrovsky and Steffen Prohaska and Gregory S.X.E. Jefferis},
keywords = {neuroinformatics, NBLAST, neuron similarity, cell type, clustering, online resource},
abstract = {Summary
Neural circuit mapping is generating datasets of tens of thousands of labeled neurons. New computational tools are needed to search and organize these data. We present NBLAST, a sensitive and rapid algorithm, for measuring pairwise neuronal similarity. NBLAST considers both position and local geometry, decomposing neurons into short segments; matched segments are scored using a probabilistic scoring matrix defined by statistics of matches and non-matches. We validated NBLAST on a published dataset of 16,129 single Drosophila neurons. NBLAST can distinguish neuronal types down to the finest level (single identified neurons) without a priori information. Cluster analysis of extensively studied neuronal classes identified new types and unreported topographical features. Fully automated clustering organized the validation dataset into 1,052 clusters, many of which map onto previously described neuronal types. NBLAST supports additional query types, including searching neurons against transgene expression patterns. Finally, we show that NBLAST is effective with data from other invertebrates and zebrafish.
Video Abstract
}
}
@article{sexton2025svt,
author = {Zachary A. Sexton  and Dominic Rütsche  and Jessica E. Herrmann  and Andrew R. Hudson  and Soham Sinha  and Jianyi Du  and Daniel J. Shiwarski  and Anastasiia Masaltseva  and Fredrik Samdal Solberg  and Jonathan Pham  and Jason M. Szafron  and Sean M. Wu  and Adam W. Feinberg  and Mark A. Skylar-Scott  and Alison L. Marsden },
title = {Rapid model-guided design of organ-scale synthetic vasculature for biomanufacturing},
journal = {Science},
volume = {388},
number = {6752},
pages = {1198-1204},
year = {2025},
doi = {10.1126/science.adj6152},
URL = {https://www.science.org/doi/abs/10.1126/science.adj6152},
eprint = {https://www.science.org/doi/pdf/10.1126/science.adj6152},
abstract = {Our ability to produce human-scale biomanufactured organs is limited by inadequate vascularization and perfusion. For arbitrarily complex geometries, designing and printing vasculature capable of adequate perfusion poses a major hurdle. We introduce a model-driven design platform that demonstrates rapid synthetic vascular model generation alongside multifidelity computational fluid dynamics simulations and three-dimensional bioprinting. Key algorithmic advances accelerate vascular generation 230-fold and enable application to arbitrarily complex shapes. We demonstrate that organ-scale vascular network models can be generated and used to computationally vascularize \&gt;200 engineered and anatomic models. Synthetic vascular perfusion improves cell viability in fabricated living-tissue constructs. This platform enables the rapid, scalable vascular model generation and fluid physics analysis for biomanufactured tissues that are necessary for future scale-up and production. Whereas small clusters of cells can extract nutrients and oxygen from their surroundings, larger tissues need to have a vascular system to ensure sufficient transport and availability. This limitation is a challenge when engineering synthetic tissues because of the complexities of designing and then fabricating a vascular system. Sexton et al. developed a model-driven design platform to rapidly design vasculature trees for the generation of organ or tissue mimetics (see the Perspective by Huang and Ju). The model includes hemodynamic properties such as flow patterns and pressure in the context of branching points and hierarchical trees, enabling the fabrication of arbitrarily complex shapes. The authors demonstrate perfusion of three-dimensional bioprinted vascular networks in a bioreactor that show improved cell viability. —Marc S. Lavine}}
@misc{luo2024parse,
      title={Efficient automatic segmentation for multi-level pulmonary arteries: The PARSE challenge}, 
      author={Gongning Luo and Kuanquan Wang and Jun Liu and Shuo Li and Xinjie Liang and Xiangyu Li and Shaowei Gan and Wei Wang and Suyu Dong and Wenyi Wang and Pengxin Yu and Enyou Liu and Hongrong Wei and Na Wang and Jia Guo and Huiqi Li and Zhao Zhang and Ziwei Zhao and Na Gao and Nan An and Ashkan Pakzad and Bojidar Rangelov and Jiaqi Dou and Song Tian and Zeyu Liu and Yi Wang and Ampatishan Sivalingam and Kumaradevan Punithakumar and Zhaowen Qiu and Xin Gao},
      year={2024},
      eprint={2304.03708},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2304.03708}, 
}

@book{diestel2017graph,
  author    = {Reinhard Diestel},
  title     = {Graph Theory},
  edition   = {5},
  publisher = {Springer},
  series    = {Graduate Texts in Mathematics},
  year      = {2017},
  isbn      = {978-3-319-41320-4}
}
@Article{Breiman2001RF,
author={Breiman, Leo},
title={Random Forests},
journal={Machine Learning},
year={2001},
month={Oct},
day={01},
volume={45},
number={1},
pages={5-32},
abstract={Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund {\&} R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ***, 148--156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.},
issn={1573-0565},
doi={10.1023/A:1010933404324},
url={https://doi.org/10.1023/A:1010933404324}
}
@article{Frangi2000,
author = {Frangi, Alejandro and Niessen, W.J. and Vincken, Koen and Viergever, Max},
year = {2000},
month = {02},
pages = {},
title = {Multiscale Vessel Enhancement Filtering},
volume = {1496},
journal = {Med. Image Comput. Comput. Assist. Interv.}
}

@inproceedings{hirsch2020_an_auxil_task_for_learn_nucle_segme_in_3d,
  author =       {Hirsch, Peter and Kainmueller, Dagmar},
  title =        {An Auxiliary Task for Learning Nuclei Segmentation in 3D
                  Microscopy Images},
  booktitle =    {International Conference on Medical Imaging with Deep
                  Learning, {MIDL} 2020, 6-8 July 2020, Montr{\'{e}}al, QC,
                  Canada},
  series =       {Proceedings of Machine Learning Research},
  volume =       121,
  pages =        {304--321},
  publisher =    {{PMLR}},
  year =         2020,
  url =          {http://proceedings.mlr.press/v121/hirsch20a.html},
  timestamp =    {Fri, 27 Nov 2020 16:13:27 +0100},
  biburl =       {https://dblp.org/rec/conf/midl/HirschK20.bib},
  bibsource =    {dblp computer science bibliography, https://dblp.org}
}

@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 US New York}
}
