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：精确分割管状拓扑结构（如血管和道路）对多个领域至关重要，可确保下游任务的准确性和效率。然而，诸多因素（包括细长局部结构和多变的全局形态）使该任务变得复杂。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时增强感知能力。首先，我们提出一种动态蛇形卷积，通过自适应聚焦细长曲折的局部结构来精确捕捉管状结构的特征。随后，提出一种多视角特征融合策略，在特征融合过程中从多角度补充注意力，确保保留不同全局形态下的重要信息。最后，基于持续同调提出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。在二维和三维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中具有更高的准确性和连续性。我们的代码将公开提供。