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Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA

Fuente: arXiv

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Hauptverfasser:	Yang, Kaiyuan, Musio, Fabio, Ma, Yihui, Juchler, Norman, Paetzold, Johannes C., Al-Maskari, Rami, Höher, Luciano, Li, Hongwei Bran, Hamamci, Ibrahim Ethem, Sekuboyina, Anjany, Shit, Suprosanna, Huang, Houjing, Prabhakar, Chinmay, de la Rosa, Ezequiel, Wittmann, Bastian, Waldmannstetter, Diana, Kofler, Florian, Navarro, Fernando, Menten, Martin, Ezhov, Ivan, Rueckert, Daniel, Vos, Iris N., Ruigrok, Ynte M., Velthuis, Birgitta K., Kuijf, Hugo J., Shi, Pengcheng, Liu, Wei, Ma, Ting, Rokuss, Maximilian R., Kirchhoff, Yannick, Isensee, Fabian, Maier-Hein, Klaus, Zhu, Chengcheng, Zhao, Huilin, Bijlenga, Philippe, Hämmerli, Julien, Wurster, Catherine, Westphal, Laura, Bisschop, Jeroen, Colombo, Elisa, Baazaoui, Hakim, Handelsmann, Hannah-Lea, Makmur, Andrew, Hallinan, James, Soundararajan, Amrish, Wiestler, Bene, Kirschke, Jan S., Wiest, Roland, Montagnon, Emmanuel, Letourneau-Guillon, Laurent, Oh, Kwanseok, Lee, Dahye, Hilbert, Adam, Aydin, Orhun Utku, Rallios, Dimitrios, Rieger, Jana, Tanioka, Satoru, Koch, Alexander, Frey, Dietmar, Qayyum, Abdul, Mazher, Moona, Niederer, Steven, Disch, Nico, Holzschuh, Julius, LaBella, Dominic, Galati, Francesco, Falcetta, Daniele, Zuluaga, Maria A., Lin, Chaolong, Zhao, Haoran, Zhang, Zehan, Zhang, Minghui, You, Xin, Zhang, Hanxiao, Yang, Guang-Zhong, Gu, Yun, Ra, Sinyoung, Hwang, Jongyun, Park, Hyunjin, Chen, Junqiang, Wodzinski, Marek, Müller, Henning, Mansouri, Nesrin, Autrusseau, Florent, Yalçin, Cansu, Hamadache, Rachika E., Lisazo, Clara, Salvi, Joaquim, Casamitjana, Adrià, Lladó, Xavier, Estrada, Uma Maria Lal-Trehan, Abramova, Valeriia, Giancardo, Luca, Oliver, Arnau, Casademunt, Paula, Galdran, Adrian, Delucchi, Matteo, Liu, Jialu, Huang, Haibin, Cui, Yue, Lin, Zehang, Liu, Yusheng, Zhu, Shunzhi, Patel, Tatsat R., Siddiqui, Adnan H., Tutino, Vincent M., Orouskhani, Maysam, Wang, Huayu, Mossa-Basha, Mahmud, Sato, Yuki, Hirsch, Sven, Wegener, Susanne, Menze, Bjoern
Format:	Preprint
Veröffentlicht:	2023
Schlagworte:	Computer Vision and Pattern Recognition Machine Learning Quantitative Methods Tissues and Organs
Online-Zugang:	Acceder al recurso
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_version_	1866913930700390400
author	Yang, Kaiyuan Musio, Fabio Ma, Yihui Juchler, Norman Paetzold, Johannes C. Al-Maskari, Rami Höher, Luciano Li, Hongwei Bran Hamamci, Ibrahim Ethem Sekuboyina, Anjany Shit, Suprosanna Huang, Houjing Prabhakar, Chinmay de la Rosa, Ezequiel Wittmann, Bastian Waldmannstetter, Diana Kofler, Florian Navarro, Fernando Menten, Martin Ezhov, Ivan Rueckert, Daniel Vos, Iris N. Ruigrok, Ynte M. Velthuis, Birgitta K. Kuijf, Hugo J. Shi, Pengcheng Liu, Wei Ma, Ting Rokuss, Maximilian R. Kirchhoff, Yannick Isensee, Fabian Maier-Hein, Klaus Zhu, Chengcheng Zhao, Huilin Bijlenga, Philippe Hämmerli, Julien Wurster, Catherine Westphal, Laura Bisschop, Jeroen Colombo, Elisa Baazaoui, Hakim Handelsmann, Hannah-Lea Makmur, Andrew Hallinan, James Soundararajan, Amrish Wiestler, Bene Kirschke, Jan S. Wiest, Roland Montagnon, Emmanuel Letourneau-Guillon, Laurent Oh, Kwanseok Lee, Dahye Hilbert, Adam Aydin, Orhun Utku Rallios, Dimitrios Rieger, Jana Tanioka, Satoru Koch, Alexander Frey, Dietmar Qayyum, Abdul Mazher, Moona Niederer, Steven Disch, Nico Holzschuh, Julius LaBella, Dominic Galati, Francesco Falcetta, Daniele Zuluaga, Maria A. Lin, Chaolong Zhao, Haoran Zhang, Zehan Zhang, Minghui You, Xin Zhang, Hanxiao Yang, Guang-Zhong Gu, Yun Ra, Sinyoung Hwang, Jongyun Park, Hyunjin Chen, Junqiang Wodzinski, Marek Müller, Henning Mansouri, Nesrin Autrusseau, Florent Yalçin, Cansu Hamadache, Rachika E. Lisazo, Clara Salvi, Joaquim Casamitjana, Adrià Lladó, Xavier Estrada, Uma Maria Lal-Trehan Abramova, Valeriia Giancardo, Luca Oliver, Arnau Casademunt, Paula Galdran, Adrian Delucchi, Matteo Liu, Jialu Huang, Haibin Cui, Yue Lin, Zehang Liu, Yusheng Zhu, Shunzhi Patel, Tatsat R. Siddiqui, Adnan H. Tutino, Vincent M. Orouskhani, Maysam Wang, Huayu Mossa-Basha, Mahmud Sato, Yuki Hirsch, Sven Wegener, Susanne Menze, Bjoern
author_facet	Yang, Kaiyuan Musio, Fabio Ma, Yihui Juchler, Norman Paetzold, Johannes C. Al-Maskari, Rami Höher, Luciano Li, Hongwei Bran Hamamci, Ibrahim Ethem Sekuboyina, Anjany Shit, Suprosanna Huang, Houjing Prabhakar, Chinmay de la Rosa, Ezequiel Wittmann, Bastian Waldmannstetter, Diana Kofler, Florian Navarro, Fernando Menten, Martin Ezhov, Ivan Rueckert, Daniel Vos, Iris N. Ruigrok, Ynte M. Velthuis, Birgitta K. Kuijf, Hugo J. Shi, Pengcheng Liu, Wei Ma, Ting Rokuss, Maximilian R. Kirchhoff, Yannick Isensee, Fabian Maier-Hein, Klaus Zhu, Chengcheng Zhao, Huilin Bijlenga, Philippe Hämmerli, Julien Wurster, Catherine Westphal, Laura Bisschop, Jeroen Colombo, Elisa Baazaoui, Hakim Handelsmann, Hannah-Lea Makmur, Andrew Hallinan, James Soundararajan, Amrish Wiestler, Bene Kirschke, Jan S. Wiest, Roland Montagnon, Emmanuel Letourneau-Guillon, Laurent Oh, Kwanseok Lee, Dahye Hilbert, Adam Aydin, Orhun Utku Rallios, Dimitrios Rieger, Jana Tanioka, Satoru Koch, Alexander Frey, Dietmar Qayyum, Abdul Mazher, Moona Niederer, Steven Disch, Nico Holzschuh, Julius LaBella, Dominic Galati, Francesco Falcetta, Daniele Zuluaga, Maria A. Lin, Chaolong Zhao, Haoran Zhang, Zehan Zhang, Minghui You, Xin Zhang, Hanxiao Yang, Guang-Zhong Gu, Yun Ra, Sinyoung Hwang, Jongyun Park, Hyunjin Chen, Junqiang Wodzinski, Marek Müller, Henning Mansouri, Nesrin Autrusseau, Florent Yalçin, Cansu Hamadache, Rachika E. Lisazo, Clara Salvi, Joaquim Casamitjana, Adrià Lladó, Xavier Estrada, Uma Maria Lal-Trehan Abramova, Valeriia Giancardo, Luca Oliver, Arnau Casademunt, Paula Galdran, Adrian Delucchi, Matteo Liu, Jialu Huang, Haibin Cui, Yue Lin, Zehang Liu, Yusheng Zhu, Shunzhi Patel, Tatsat R. Siddiqui, Adnan H. Tutino, Vincent M. Orouskhani, Maysam Wang, Huayu Mossa-Basha, Mahmud Sato, Yuki Hirsch, Sven Wegener, Susanne Menze, Bjoern
contents	The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neurovascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two non-invasive angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited datasets with annotations on CoW anatomy, especially for CTA. Therefore, we organized the TopCoW challenge with the release of an annotated CoW dataset. The TopCoW dataset is the first public dataset with voxel-level annotations for 13 CoW vessel components, enabled by virtual reality technology. It is also the first large dataset using 200 pairs of MRA and CTA from the same patients. As part of the benchmark, we invited submissions worldwide and attracted over 250 registered participants from six continents. The submissions were evaluated on both internal and external test datasets of 226 scans from over five centers. The top performing teams achieved over 90% Dice scores at segmenting the CoW components, over 80% F1 scores at detecting key CoW components, and over 70% balanced accuracy at classifying CoW variants for nearly all test sets. The best algorithms also showed clinical potential in classifying fetal-type posterior cerebral artery and locating aneurysms with CoW anatomy. TopCoW demonstrated the utility and versatility of CoW segmentation algorithms for a wide range of downstream clinical applications with explainability. The annotated datasets and best performing algorithms have been released as public Zenodo records to foster further methodological development and clinical tool building.
format	Preprint
id	arxiv_https___arxiv_org_abs_2312_17670
institution	arXiv
publishDate	2023
record_format	arxiv
spellingShingle	Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA Yang, Kaiyuan Musio, Fabio Ma, Yihui Juchler, Norman Paetzold, Johannes C. Al-Maskari, Rami Höher, Luciano Li, Hongwei Bran Hamamci, Ibrahim Ethem Sekuboyina, Anjany Shit, Suprosanna Huang, Houjing Prabhakar, Chinmay de la Rosa, Ezequiel Wittmann, Bastian Waldmannstetter, Diana Kofler, Florian Navarro, Fernando Menten, Martin Ezhov, Ivan Rueckert, Daniel Vos, Iris N. Ruigrok, Ynte M. Velthuis, Birgitta K. Kuijf, Hugo J. Shi, Pengcheng Liu, Wei Ma, Ting Rokuss, Maximilian R. Kirchhoff, Yannick Isensee, Fabian Maier-Hein, Klaus Zhu, Chengcheng Zhao, Huilin Bijlenga, Philippe Hämmerli, Julien Wurster, Catherine Westphal, Laura Bisschop, Jeroen Colombo, Elisa Baazaoui, Hakim Handelsmann, Hannah-Lea Makmur, Andrew Hallinan, James Soundararajan, Amrish Wiestler, Bene Kirschke, Jan S. Wiest, Roland Montagnon, Emmanuel Letourneau-Guillon, Laurent Oh, Kwanseok Lee, Dahye Hilbert, Adam Aydin, Orhun Utku Rallios, Dimitrios Rieger, Jana Tanioka, Satoru Koch, Alexander Frey, Dietmar Qayyum, Abdul Mazher, Moona Niederer, Steven Disch, Nico Holzschuh, Julius LaBella, Dominic Galati, Francesco Falcetta, Daniele Zuluaga, Maria A. Lin, Chaolong Zhao, Haoran Zhang, Zehan Zhang, Minghui You, Xin Zhang, Hanxiao Yang, Guang-Zhong Gu, Yun Ra, Sinyoung Hwang, Jongyun Park, Hyunjin Chen, Junqiang Wodzinski, Marek Müller, Henning Mansouri, Nesrin Autrusseau, Florent Yalçin, Cansu Hamadache, Rachika E. Lisazo, Clara Salvi, Joaquim Casamitjana, Adrià Lladó, Xavier Estrada, Uma Maria Lal-Trehan Abramova, Valeriia Giancardo, Luca Oliver, Arnau Casademunt, Paula Galdran, Adrian Delucchi, Matteo Liu, Jialu Huang, Haibin Cui, Yue Lin, Zehang Liu, Yusheng Zhu, Shunzhi Patel, Tatsat R. Siddiqui, Adnan H. Tutino, Vincent M. Orouskhani, Maysam Wang, Huayu Mossa-Basha, Mahmud Sato, Yuki Hirsch, Sven Wegener, Susanne Menze, Bjoern Computer Vision and Pattern Recognition Machine Learning Quantitative Methods Tissues and Organs The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neurovascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two non-invasive angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited datasets with annotations on CoW anatomy, especially for CTA. Therefore, we organized the TopCoW challenge with the release of an annotated CoW dataset. The TopCoW dataset is the first public dataset with voxel-level annotations for 13 CoW vessel components, enabled by virtual reality technology. It is also the first large dataset using 200 pairs of MRA and CTA from the same patients. As part of the benchmark, we invited submissions worldwide and attracted over 250 registered participants from six continents. The submissions were evaluated on both internal and external test datasets of 226 scans from over five centers. The top performing teams achieved over 90% Dice scores at segmenting the CoW components, over 80% F1 scores at detecting key CoW components, and over 70% balanced accuracy at classifying CoW variants for nearly all test sets. The best algorithms also showed clinical potential in classifying fetal-type posterior cerebral artery and locating aneurysms with CoW anatomy. TopCoW demonstrated the utility and versatility of CoW segmentation algorithms for a wide range of downstream clinical applications with explainability. The annotated datasets and best performing algorithms have been released as public Zenodo records to foster further methodological development and clinical tool building.
title	Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA
topic	Computer Vision and Pattern Recognition Machine Learning Quantitative Methods Tissues and Organs
url	https://arxiv.org/abs/2312.17670