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 and invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. TopCoW dataset was the first public dataset with voxel-level annotations for CoW's 13 vessel components, made possible by virtual-reality (VR) technology. It was also the first dataset with paired MRA and CTA from the same patients. TopCoW challenge aimed to tackle the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. 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's topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically.

翻译：Willis环（CoW）是连接大脑主要循环系统的重要动脉网络，其血管结构被认为会影响严重神经血管疾病的风险、严重程度和临床预后。然而，表征高度变异的CoW解剖结构至今仍是需要人工耗时处理的专业任务。CoW通常通过磁共振血管成像（MRA）和计算机断层扫描血管成像（CTA）两种血管造影模态成像，但现有包含CoW解剖标注的公开数据集极其有限，尤其是CTA数据。为此，我们于2023年组织了TopCoW挑战赛，发布了带标注的CoW数据集，并向全球征集CoW分割任务方案，吸引了来自四大洲超过140名注册参与者。TopCoW数据集是首个对CoW的13个血管组件进行体素级标注的公开数据集，这一成果得益于虚拟现实（VR）技术。该数据集也是首个包含同一患者配对的MRA与CTA数据的数据集。TopCoW挑战赛旨在于将CoW表征问题作为一项强调拓扑指标的多类解剖分割任务进行攻关。表现最佳的团队成功将多数CoW组件的Dice分数提升至约90%，但交通动脉和罕见变异的分数较低。部分高Dice得分的预测结果仍存在拓扑错误。进一步的拓扑分析揭示了在检测特定CoW组件和精确匹配CoW变异拓扑结构方面仍存在改进空间。TopCoW首次从形态学和拓扑学两个维度为MRA与CTA的CoW解剖分割任务建立了基准评估框架。