Accurate segmentation of topological tubular structures, such as blood vessels and roads, is crucial in various fields, ensuring accuracy and efficiency in downstream tasks. However, many factors complicate the task, including thin local structures and variable global morphologies. In this work, we note the specificity of tubular structures and use this knowledge to guide our DSCNet to simultaneously enhance perception in three stages: feature extraction, feature fusion, and loss constraint. First, we propose a dynamic snake convolution to accurately capture the features of tubular structures by adaptively focusing on slender and tortuous local structures. Subsequently, we propose a multi-view feature fusion strategy to complement the attention to features from multiple perspectives during feature fusion, ensuring the retention of important information from different global morphologies. Finally, a continuity constraint loss function, based on persistent homology, is proposed to constrain the topological continuity of the segmentation better. Experiments on 2D and 3D datasets show that our DSCNet provides better accuracy and continuity on the tubular structure segmentation task compared with several methods. Our codes will be publicly available.

翻译：精确分割拓扑管状结构（如血管和道路）在各个领域至关重要，可确保下游任务的准确性和效率。然而，诸多因素使该任务复杂化，包括细小的局部结构和变化的全局形态。本文注意到管状结构的特异性，并利用这一知识指导我们的DSCNet在特征提取、特征融合和损失约束三个阶段同步增强感知能力。首先，我们提出一种动态蛇形卷积，通过自适应地聚焦于细长且蜿蜒的局部结构，准确捕捉管状结构特征。随后，我们提出一种多视角特征融合策略，在特征融合阶段从多个视角补充对特征的关注，确保保留来自不同全局形态的重要信息。最后，我们提出一种基于持久同调的连续性约束损失函数，以更好地约束分割结果的拓扑连续性。在2D和3D数据集上的实验表明，与多种方法相比，我们的DSCNet在管状结构分割任务上具有更高的准确性和连续性。我们的代码将公开提供。