发表机构
Sano – Centre for Computational Personalised Medicine International Research Foundation; AGH University of Krakow（Sano – 计算个性化医学国际研究基金会中心; AGH科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对血管分割中局部误差破坏连通性的问题，提出网格引导的后处理框架，通过可变形网格拟合与保守重连，显著提升连通性（如ccDice从0.028升至0.862）而不损重叠分数。
AI 中文摘要
血管分割通常被优化为逐体素分类，但小的局部误差可能严重破坏血管连通性，同时对重叠分数影响甚微。这对于依赖中心线、分支、连通分量或图结构的下游分析尤其成问题。我们提出了一种网格引导的后处理框架，用于修复由nnU-Net产生的断裂血管分割。对于每个预测的二元掩膜，一个可变形模板网格在物理空间中拟合到掩膜表面，并用作特定病例的几何支架。拟合的网格不被体素化为最终分割；相反，它在前景生长约束下，通过提出或验证细桥候选来引导断开分量的保守重连。我们使用AortaSeg24和SEGA评估主动脉、TopCoW评估Willis环、PARSE评估肺动脉，在三种血管解剖结构中评估了该方法。性能通过Dice、连通分量Dice（ccDice）和Betti-0数来衡量。在这些数据集上，修复显著改善了连通性，同时保持了重叠：Dice几乎不变，而ccDice从主动脉的0.596增加到0.992，从TopCoW的0.722增加到0.835，从PARSE的0.028增加到0.862。FOMAML元初始化进一步加速了每例的拟合，支持了基于网格的血管拓扑修复的实用性。这些结果表明，显式网格表示可以为纠正原本准确的体素分割中的拓扑失败提供有用的几何先验。
英文摘要
Vessel segmentation is commonly optimized as voxel-wise classification, but small local errors can strongly disrupt vascular connectivity while having little effect on overlap scores. This is particularly problematic for downstream analyses that rely on centerlines, branches, connected components, or graph structure. We propose a mesh-guided post-processing framework for repairing broken vessel segmentations produced by nnU-Net. For each predicted binary mask, a deformable template mesh is fitted to the mask surface in physical space and used as a case-specific geometric scaffold. The fitted mesh is not voxelized as the final segmentation; instead, it guides conservative reconnection of disconnected components by proposing or validating thin bridge candidates under foreground-growth constraints. We evaluated this approach in three vascular anatomies using AortaSeg24 and SEGA for the aorta, TopCoW for the Circle of Willis, and PARSE for the pulmonary arteries. Performance is measured using Dice, connected-component Dice (ccDice), and the Betti-0 number. Across these datasets, repair substantially improved connectivity while preserving overlap: Dice remained nearly unchanged, whereas ccDice increased from 0.596 to 0.992 for aorta, from 0.722 to 0.835 for TopCoW, and from 0.028 to 0.862 for PARSE. The FOMAML meta-initialization further accelerated the fitting per-case, supporting practical mesh-based repair of the vascular topology. These results suggest that explicit mesh representations can provide a useful geometric prior for correcting topological failures in otherwise accurate voxel segmentations.
CommentsAccepted at ShapeMI 2026 (Shape in Medical Imaging), MICCAI 2026 Workshop. 17 pages, 5 figures, 3 tables
