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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; Waldmannstetter, Diana; Kofler, Florian; Navarro, Fernando; Menten, Martin; Ezhov, Ivan; Rueckert, Daniel; Vos, Iris; Ruigrok, Ynte; Velthuis, Birgitta; Kuijf, Hugo; Hämmerli, Julien; Wurster, Catherine; Bijlenga, Philippe; Westphal, Laura; Bisschop, Jeroen; Colombo, Elisa; Baazaoui, Hakim; Makmur, Andrew; Hallinan, James; Wiestler, Bene; Kirschke, Jan S; Wiest, Roland; Montagnon, Emmanuel; Letourneau-Guillon, Laurent; Galdran, Adrian; Galati, Francesco; Falcetta, Daniele; Zuluaga, Maria A; Lin, Chaolong; Zhao, Haoran; Zhang, Zehan; Ra, Sinyoung; Hwang, Jongyun; Park, Hyunjin; Chen, Junqiang; Wodzinski, Marek.
Afiliação
  • Yang K; Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
  • Musio F; Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
  • Ma Y; Center for Computational Health, Zurich University of Applied Sciences, Zurich, Switzerland.
  • Juchler N; Department of Neuroradiology, University Hospital of Zurich, Zurich, Switzerland.
  • Paetzold JC; Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China.
  • Al-Maskari R; Center for Computational Health, Zurich University of Applied Sciences, Zurich, Switzerland.
  • Höher L; Department of Computing, Imperial College London, London, UK.
  • Li HB; Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Munich, Germany.
  • Hamamci IE; Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Munich, Germany.
  • Sekuboyina A; Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center, Munich, Germany.
  • Shit S; Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
  • Huang H; Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, USA.
  • Prabhakar C; Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
  • de la Rosa E; Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
  • Waldmannstetter D; Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
  • Kofler F; School of Medicine, Technical University of Munich, Munich, Germany.
  • Navarro F; Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
  • Menten M; Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
  • Ezhov I; Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
  • Rueckert D; Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
  • Vos I; Department of Informatics, Technical University of Munich, Munich, Germany.
  • Ruigrok Y; School of Medicine, Technical University of Munich, Munich, Germany.
  • Velthuis B; Department of Informatics, Technical University of Munich, Munich, Germany.
  • Kuijf H; Helmholtz AI, Helmholtz Munich, Munich, Germany.
  • Hämmerli J; School of Medicine, Technical University of Munich, Munich, Germany.
  • Wurster C; Department of Informatics, Technical University of Munich, Munich, Germany.
  • Bijlenga P; Department of Informatics, Technical University of Munich, Munich, Germany.
  • Westphal L; Department of Computing, Imperial College London, London, UK.
  • Bisschop J; Department of Informatics, Technical University of Munich, Munich, Germany.
  • Colombo E; Department of Informatics, Technical University of Munich, Munich, Germany.
  • Baazaoui H; Department of Computing, Imperial College London, London, UK.
  • Makmur A; Image Sciences Institute, UMC Utrecht, Utrecht, the Netherlands.
  • Hallinan J; Department of Neurology, UMC Utrecht, Utrecht, the Netherlands.
  • Wiestler B; Department of Radiology, UMC Utrecht, Utrecht, the Netherlands.
  • Kirschke JS; Image Sciences Institute, UMC Utrecht, Utrecht, the Netherlands.
  • Wiest R; Department of Clinical Neurosciences, Division of Neurosurgery, Geneva University Hospitals, Geneva, Switzerland.
  • Montagnon E; Department of Clinical Neurosciences, Division of Neurosurgery, Geneva University Hospitals, Geneva, Switzerland.
  • Letourneau-Guillon L; Department of Clinical Neurosciences, Division of Neurosurgery, Geneva University Hospitals, Geneva, Switzerland.
  • Galdran A; Department of Neurology, University Hospital of Zurich, Zurich, Switzerland.
  • Galati F; Department of Physiology, University of Toronto, Toronto, Canada.
  • Falcetta D; Department of Neurosurgery, University Hospital of Zurich, Zurich, Switzerland.
  • Zuluaga MA; Department of Neurology, University Hospital of Zurich, Zurich, Switzerland.
  • Lin C; Department of Diagnostic Imaging, National University Hospital, Singapore.
  • Zhao H; Department of Diagnostic Imaging, National University Hospital, Singapore.
  • Zhang Z; School of Medicine, Technical University of Munich, Munich, Germany.
  • Ra S; School of Medicine, Technical University of Munich, Munich, Germany.
  • Hwang J; Department of Diagnostic and Interventional Neuroradiology, University Hospital Berne and University of Berne, Berne, Switzerland.
  • Park H; Centre de Recherche du Centre Hospitalier de l'Université de Montreal (CRCHUM), Montreal, Canada.
  • Chen J; Centre de Recherche du Centre Hospitalier de l'Université de Montreal (CRCHUM), Montreal, Canada.
  • Wodzinski M; Universitat Pompeu Fabra, Barcelona, Spain.
ArXiv ; 2024 Apr 29.
Article em En | MEDLINE | ID: mdl-38235066
ABSTRACT
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 neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Guideline / Prognostic_studies Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Guideline / Prognostic_studies Idioma: En Ano de publicação: 2024 Tipo de documento: Article