Haili Ye;Xiaoqing Zhang;Yan Hu;Huazhu Fu;Jiang Liu
{"title":"VSR-Net: Vessel-Like Structure Rehabilitation Network With Graph Clustering","authors":"Haili Ye;Xiaoqing Zhang;Yan Hu;Huazhu Fu;Jiang Liu","doi":"10.1109/TIP.2025.3526061","DOIUrl":null,"url":null,"abstract":"The morphologies of vessel-like structures, such as blood vessels and nerve fibres, play significant roles in disease diagnosis, e.g., Parkinson’s disease. Although deep network-based refinement segmentation and topology-preserving segmentation methods recently have achieved promising results in segmenting vessel-like structures, they still face two challenges: 1) existing methods often have limitations in rehabilitating subsection ruptures in segmented vessel-like structures; 2) they are typically 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 perspective, we propose a novel Vessel-like Structure Rehabilitation Network (VSR-Net) to both rehabilitate subsection ruptures and improve the model calibration based on coarse vessel-like structure segmentation results. VSR-Net first constructs subsection rupture clusters via a 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 six 2D/3D medical image datasets show that VSR-Net significantly outperforms state-of-the-art (SOTA) refinement segmentation methods with lower calibration errors. Additionally, we provide quantitative analysis to explain the morphological difference between the VSR-Net’s rehabilitation results and ground truth (GT), which are smaller compared to those between SOTA methods and GT, demonstrating that our method more effectively rehabilitates vessel-like structures.","PeriodicalId":94032,"journal":{"name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","volume":"34 ","pages":"1090-1105"},"PeriodicalIF":0.0000,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10871930/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The morphologies of vessel-like structures, such as blood vessels and nerve fibres, play significant roles in disease diagnosis, e.g., Parkinson’s disease. Although deep network-based refinement segmentation and topology-preserving segmentation methods recently have achieved promising results in segmenting vessel-like structures, they still face two challenges: 1) existing methods often have limitations in rehabilitating subsection ruptures in segmented vessel-like structures; 2) they are typically 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 perspective, we propose a novel Vessel-like Structure Rehabilitation Network (VSR-Net) to both rehabilitate subsection ruptures and improve the model calibration based on coarse vessel-like structure segmentation results. VSR-Net first constructs subsection rupture clusters via a 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 six 2D/3D medical image datasets show that VSR-Net significantly outperforms state-of-the-art (SOTA) refinement segmentation methods with lower calibration errors. Additionally, we provide quantitative analysis to explain the morphological difference between the VSR-Net’s rehabilitation results and ground truth (GT), which are smaller compared to those between SOTA methods and GT, demonstrating that our method more effectively rehabilitates vessel-like structures.
血管和神经纤维等血管样结构的形态学在帕金森病等疾病的诊断中起着重要作用。尽管近年来基于深度网络的精细分割和拓扑保持分割方法在类血管结构分割方面取得了令人鼓舞的成果,但它们仍然面临两个挑战:1)现有方法在修复类血管结构分段破裂方面存在局限性;2)他们通常对预测的分割结果过于自信。为了应对这两个挑战,本文试图从结构修复的角度利用分段断裂之间的空间互联关系的潜力。基于这一观点,我们提出了一种新的血管样结构修复网络(VSR-Net),既可以修复分段破裂,又可以改进基于粗糙血管样结构分割结果的模型校准。vrr - net首先通过曲线聚类模块(CCM)构建分段破裂簇。然后,采用精心设计的曲线合并模块(CMM)对分段断裂进行修复,得到精细化的血管状结构;在6个2D/3D医学图像数据集上进行的大量实验表明,VSR-Net以更低的校准误差显著优于最先进的(SOTA)精细分割方法。此外,我们提供了定量分析来解释VSR-Net的修复结果和ground truth (GT)之间的形态学差异,与SOTA方法和GT之间的差异相比,前者较小,表明我们的方法更有效地修复了血管样结构。