用递归精修提升脑底动脉环分割的拓扑连通性
Anatomically Conditioned Recurrent Refinement for Topology-Aware Circle of Willis Segmentation
- 分两路处理：静态特征提取+动态拓扑修正
- 霍斯多夫距离降为4.72毫米，拓扑错误减少52.5%
- 适合血管精细分割任务，尤其关注结构完整性的研究
从磁共振血管成像（MRA）中分割脑底动脉环（CoW）极具挑战，因其拓扑结构复杂且血管细小，易出现断裂。标准卷积神经网络难以捕捉拓扑约束，导致“断流”伪影。为此，我们提出解剖条件递归精修U-Net（AC2RUNet），将分割分为两个分支：静态分支提取不变解剖特征，轻量动态分支通过迭代过程逐步修正拓扑错误。同时引入动态课程学习策略，由高召回几何监督逐步过渡到拓扑感知约束。在TopCoW数据集上验证，相比nnU-Net基线，AC2RUNet将霍斯多夫距离从9.17毫米降至4.72毫米，贝蒂数误差由0.40降至0.19，显著提升拓扑连通性，同时保持相当的体积相似度（Dice）。
原文摘要 · Abstract (English)
Segmenting the Circle of Willis (CoW) from Magnetic Resonance Angiography (MRA) is challenging due to complex topology and thin vascular structures that are prone to fragmentation. Standard Convolutional Neural Networks (CNNs) often fail to capture these topological constraints, resulting in "broken vessel" artifacts. To address this, we propose the Anatomically Conditioned Recurrent Refinement U-Net (AC2RUNet). Our architecture decouples segmentation into two streams: a Static Stream that extracts invariant anatomical features and a lightweight Dynamic Stream that iteratively refines topological errors over time. We further introduce a dynamic curriculum learning strategy that transitions from high-recall geometric supervision to topology-aware constraints. Validated on the TopCoW dataset, AC2RUNet substantially reduces Hausdorff Distance (4.72 mm vs 9.17 mm) and Betti number errors (0.19 vs 0.40), improving topological connectivity over the nnU-Net baseline while maintaining comparable volumetric Dice.
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