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The morphologies of vessel-like structures, such as blood vessels and nervefibres, play significant roles in disease diagnosis, e.g., Parkinson's disease.Deep network-based refinement segmentation methods have recently achievedpromising vessel-like structure segmentation results. There are still twochallenges: (1) existing methods have limitations in rehabilitating subsectionruptures in segmented vessel-like structures; (2) they are often overconfidentin predicted segmentation results. To tackle these two challenges, this paperattempts to leverage the potential of spatial interconnection relationshipsamong subsection ruptures from the structure rehabilitation perspective. Basedon this, we propose a novel Vessel-like Structure Rehabilitation Network(VSR-Net) to rehabilitate subsection ruptures and improve the model calibrationbased on coarse vessel-like structure segmentation results. VSR-Net firstconstructs subsection rupture clusters with Curvilinear Clustering Module(CCM). Then, the well-designed Curvilinear Merging Module (CMM) is applied torehabilitate the subsection ruptures to obtain the refined vessel-likestructures. Extensive experiments on five 2D/3D medical image datasets showthat VSR-Net significantly outperforms state-of-the-art (SOTA) refinementsegmentation methods with lower calibration error. Additionally, we providequantitative analysis to explain the morphological difference between therehabilitation results of VSR-Net and ground truth (GT), which is smaller thanSOTA methods and GT, demonstrating that our method better rehabilitatesvessel-like structures by restoring subsection ruptures.

VSR-Net: Vessel-like Structure Rehabilitation Network with Graph Clustering