PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation
Journal:
arXiv
Published Date:
Jul 5, 2025
Abstract
Accurate vessel segmentation is crucial to assist in clinical diagnosis by
medical experts. However,
the intricate tree-like tubular structure of blood vessels poses significant
challenges for existing
segmentation algorithms. Small vascular branches are often overlooked due to
their low contrast
compared to surrounding tissues, leading to incomplete vessel segmentation.
Furthermore, the
complex vascular topology prevents the model from accurately capturing and
reconstructing vascular
structure, resulting in incorrect topology, such as breakpoints at the
bifurcation of the vascular tree.
To overcome these challenges, we propose a novel vessel segmentation
framework called PASC Net. It includes two key modules: a plug-and-play shape
self-learning convolutional (SSL) module
that optimizes convolution kernel design, and a hierarchical topological
constraint (HTC) module
that ensures vascular connectivity through topological constraints.
Specifically, the SSL module
enhances adaptability to vascular structures by optimizing conventional
convolutions into learnable
strip convolutions, which improves the network's ability to perceive
fine-grained features of tubular
anatomies. Furthermore, to better preserve the coherence and integrity of
vascular topology, the HTC
module incorporates hierarchical topological constraints-spanning linear,
planar, and volumetric
levels-which serve to regularize the network's representation of vascular
continuity and structural
consistency. We replaced the standard convolutional layers in U-Net, FCN,
U-Mamba, and nnUNet
with SSL convolutions, leading to consistent performance improvements across
all architectures.
Furthermore, when integrated into the nnUNet framework, our method
outperformed other methods
on multiple metrics, achieving state-of-the-art vascular segmentation
performance.
