VSR-Net: Vessel-like Structure Rehabilitation Network with Graph
  Clustering
Haili Ye Xiaoqing Zhang Yan Hu Huazhu Fu Jiang Liu
The morphologies of vessel-like structures, such as blood vessels and nerve
fibres, play significant roles in disease diagnosis, e.g., Parkinson's disease.
Deep network-based refinement segmentation methods have recently achieved
promising vessel-like structure segmentation results. There are still two
challenges: (1) existing methods have limitations in rehabilitating subsection
ruptures in segmented vessel-like structures; (2) they are often overconfident
in predicted segmentation results. To tackle these two challenges, this paper
attempts to leverage the potential of spatial interconnection relationships
among subsection ruptures from the structure rehabilitation perspective. Based
on this, we propose a novel Vessel-like Structure Rehabilitation Network
(VSR-Net) to rehabilitate subsection ruptures and improve the model calibration
based on coarse vessel-like structure segmentation results. VSR-Net first
constructs subsection rupture clusters with Curvilinear Clustering Module
(CCM). Then, the well-designed Curvilinear Merging Module (CMM) is applied to
rehabilitate the subsection ruptures to obtain the refined vessel-like
structures. Extensive experiments on five 2D/3D medical image datasets show
that VSR-Net significantly outperforms state-of-the-art (SOTA) refinement
segmentation methods with lower calibration error. Additionally, we provide
quantitative analysis to explain the morphological difference between the
rehabilitation results of VSR-Net and ground truth (GT), which is smaller than
SOTA methods and GT, demonstrating that our method better rehabilitates
vessel-like structures by restoring subsection ruptures.