Vascular diseases have long been regarded as a significant health concern.
Accurately detecting the location, shape, and afflicted regions of blood
vessels from a diverse range of medical images has proven to be a major
challenge. Obtaining blood vessels that retain their correct topological
structures is currently a crucial research issue. Numerous efforts have sought
to reinforce neural networks' learning of vascular geometric features,
including measures to ensure the correct topological structure of the
segmentation result's vessel centerline. Typically, these methods extract
topological features from the network's segmentation result and then apply
regular constraints to reinforce the accuracy of critical components and the
overall topological structure. However, as blood vessels are three-dimensional
structures, it is essential to achieve complete local vessel segmentation,
which necessitates enhancing the segmentation of vessel boundaries.
Furthermore, current methods are limited to handling 2D blood vessel
fragmentation cases. Our proposed boundary attention module directly extracts
boundary voxels from the network's segmentation result. Additionally, we have
established an optimal connection model based on minimal surfaces to determine
the connection order between blood vessels. Our method achieves
state-of-the-art performance in 3D multi-class vascular segmentation tasks, as
evidenced by the high values of Dice Similarity Coefficient (DSC) and
Normalized Surface Dice (NSD) metrics. Furthermore, our approach improves the
Betti error, LR error, and BR error indicators of vessel richness and
structural integrity by more than 10% compared to other methods, and
effectively addresses vessel fragmentation and yields blood vessels with a more
precise topological structure.

通过在神经网络分割结果中直接提取边界体素，在最小曲面的基础上建立最优连接模型，并改进了血管的分割，为三维多分类血管分割任务带来了最先进的性能表现，同时，相比其他方法，处理血管分散和结构完整性并有效地改善了血管结构的 Betti 误差和 LR、BR 误差指标，使血管具有更精确的拓扑结构。