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.

翻译：Willis环（CoW）是连接大脑主要循环的重要动脉网络，其血管结构被认为会影响严重神经血管疾病的风险、严重程度及临床结局。然而，表征高度变异的CoW解剖结构仍是一项依赖专家人工操作的耗时任务。CoW通常通过磁共振血管成像（MRA）和计算机断层血管成像（CTA）两种血管造影模态成像，但带有CoW解剖标注的公开数据集（尤其是CTA数据）十分有限。为此，我们于2023年组织了TopCoW挑战赛，并发布了带标注的CoW数据集。TopCoW数据集是首个利用虚拟现实（VR）技术对十三种可能的CoW血管组件进行体素级标注的公开数据集，同时也是首个包含同一患者配对MRA与CTA的大规模数据集。TopCoW挑战赛将CoW表征问题形式化为以拓扑指标为重点的多类别解剖分割任务。我们面向全球征集CoW分割任务方案，吸引了来自四大洲的140余名注册参与者。最佳表现团队成功将多数CoW组件的Dice评分提升至约90%，但交通动脉和罕见变异组件的评分较低。部分高Dice评分的预测结果存在拓扑错误，进一步的拓扑分析揭示了在检测特定CoW组件及准确匹配CoW变异拓扑结构方面的改进空间。TopCoW首次尝试从形态与拓扑两个维度为MRA和CTA的CoW解剖分割任务构建基准。