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arxiv: 2312.17670 · v4 · pith:VAVOQBY4new · submitted 2023-12-29 · 💻 cs.CV · cs.LG· q-bio.QM· q-bio.TO

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

Kaiyuan Yang , Fabio Musio , Yihui Ma , Norman Juchler , Johannes C. Paetzold , Rami Al-Maskari , Luciano H\"oher , Hongwei Bran Li
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Ibrahim Ethem Hamamci Anjany Sekuboyina Suprosanna Shit Houjing Huang Chinmay Prabhakar Ezequiel de la Rosa Bastian Wittmann Diana Waldmannstetter Florian Kofler Fernando Navarro Martin Menten Ivan Ezhov Daniel Rueckert Iris N. Vos Ynte M. Ruigrok Birgitta K. Velthuis Hugo J. Kuijf Pengcheng Shi Wei Liu Ting Ma Maximilian R. Rokuss Yannick Kirchhoff Fabian Isensee Klaus Maier-Hein Chengcheng Zhu Huilin Zhao Philippe Bijlenga Julien H\"ammerli Catherine Wurster Laura Westphal Jeroen Bisschop Elisa Colombo Hakim Baazaoui Hannah-Lea Handelsmann Andrew Makmur James Hallinan Amrish Soundararajan Bene Wiestler Jan S. Kirschke Roland Wiest Emmanuel Montagnon Laurent Letourneau-Guillon Kwanseok Oh Dahye Lee Adam Hilbert Orhun Utku Aydin Dimitrios Rallios Jana Rieger Satoru Tanioka Alexander Koch Dietmar Frey Abdul Qayyum Moona Mazher Steven Niederer Nico Disch Julius Holzschuh Dominic LaBella Francesco Galati Daniele Falcetta Maria A. Zuluaga Chaolong Lin Haoran Zhao Zehan Zhang Minghui Zhang Xin You Hanxiao Zhang Guang-Zhong Yang Yun Gu Sinyoung Ra Jongyun Hwang Hyunjin Park Junqiang Chen Marek Wodzinski Henning M\"uller Nesrin Mansouri Florent Autrusseau Cansu Yal\c{c}in Rachika E. Hamadache Clara Lisazo Joaquim Salvi Adri\`a Casamitjana Xavier Llad\'o Uma Maria Lal-Trehan Estrada Valeriia Abramova Luca Giancardo Arnau Oliver Paula Casademunt Adrian Galdran Matteo Delucchi Jialu Liu Haibin Huang Yue Cui Zehang Lin Yusheng Liu Shunzhi Zhu Tatsat R. Patel Adnan H. Siddiqui Vincent M. Tutino Maysam Orouskhani Huayu Wang Mahmud Mossa-Basha Yuki Sato Sven Hirsch Susanne Wegener Bjoern Menze
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classification 💻 cs.CV cs.LGq-bio.QMq-bio.TO
keywords clinicaldatasettopcowalgorithmsanatomycomponentsdatasetsangiography
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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.

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Cited by 2 Pith papers

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  1. AG-TAL: Anatomically-Guided Topology-Aware Loss for Multiclass Segmentation of the Circle of Willis Using Large-Scale Multi-Center Datasets

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    AG-TAL loss improves multiclass Circle of Willis segmentation to 80.85% average Dice with 1-3% gains on small arteries across multi-center datasets by embedding anatomical priors into topology-aware terms.

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