  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.
