The TopCoW Challenge - Topology-Aware Circle of Willis Segmentation for CT and MR Angiography

The circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to influence the risk, severity, and outcome of serious neurovascular diseases. However, characterizing the highly variable CoW anatomy remains a manual and time-consuming expert task. The CoW is commonly imaged by two noninvasive angiographic imaging modalities - magnetic resonance angiography (MRA) and computed tomography angiography (CTA) - yet few datasets with annotated CoW anatomy exist, and there have been no established benchmarks for comparing CoW segmentation algorithms.

We organized the Topology-Aware Anatomical Segmentation of the Circle of Willis for Computed Tomography Angiography and Magnetic Resonance Angiography (TopCoW) benchmark challenge alongside the release of an annotated CoW dataset with 125 paired MRA and CTA scans from the same patients. Voxel-level annotations for 13 vessel components were created using virtual reality technology and verified by clinical experts. Participants submitted algorithms for CoW segmentation and variant classification, which we evaluated on internal and external test sets comprising 226 scans from over five centers. The benchmark includes voxel-level segmentation, CoW component detection, CoW variant classification, and two clinical application tasks.

We received submissions from over 250 participants across six continents. Top-performing teams achieved over 90% Dice similarity coefficient scores for CoW segmentation, over 80% F1 scores for detecting key vessel components, and over 70% balanced accuracy in CoW variant classification across nearly all test sets. The best algorithms also supported clinically relevant down-stream tasks by accurately classifying fetal-type posterior cerebral arteries and localizing aneurysms in relation to CoW anatomy. Notably, modality-agnostic models and topological optimization emerged as key factors in improving segmentation performance.

This benchmark demonstrated the utility of CoW segmentation algorithms for some downstream clinical applications with explainability. The annotated datasets and top-performing algorithms are published alongside this benchmark to foster methodological advances and clinical tool development. (Funded by the Digitalization Initiative of the Zurich Higher Education Institutions and the Helmut Horten Foundation.).