Vascular segmentation in medical imaging plays a crucial role in analysing
morphological and functional assessments. Traditional methods, like the
centerline Dice (clDice) loss, ensure topology preservation but falter in
capturing geometric details, especially under translation and deformation. The
combination of clDice with traditional Dice loss can lead to diameter
imbalance, favoring larger vessels. Addressing these challenges, we introduce
the centerline boundary Dice (cbDice) loss function, which harmonizes
topological integrity and geometric nuances, ensuring consistent segmentation
across various vessel sizes. cbDice enriches the clDice approach by including
boundary-aware aspects, thereby improving geometric detail recognition. It
matches the performance of the boundary difference over union (B-DoU) loss
through a mask-distance-based approach, enhancing traslation sensitivity.
Crucially, cbDice incorporates radius information from vascular skeletons,
enabling uniform adaptation to vascular diameter changes and maintaining
balance in branch growth and fracture impacts. Furthermore, we conducted a
theoretical analysis of clDice variants (cl-X-Dice). We validated cbDice's
efficacy on three diverse vascular segmentation datasets, encompassing both 2D
and 3D, and binary and multi-class segmentation. Particularly, the method
integrated with cbDice demonstrated outstanding performance on the MICCAI 2023
TopCoW Challenge dataset. Our code is made publicly available at:
this https URL

中心线边界 Dice（cbDice）损失函数结合了拓扑完整性与几何细节，通过融合边界感知方面来改善几何细节识别，通过基于掩码距离的方法与边界差异联合（B-DoU）损失功能相匹配，提高对平移的敏感性，并借助血管骨架的半径信息实现对血管直径变化的均匀适应，保持分支生长和断裂的平衡。在三个不同的血管分割数据集上验证了 cbDice 的功效，包括二维和三维、二分类和多分类分割。特别是在 MICCAI 2023 TopCoW 挑战数据集中，与 cbDice 集成的方法表现出色。