HN-Seg:a hepatic vessel segmentation approach based on hierarchical vascular morphology awareness and noisy label refine
10.3969/j.issn.1005-202X.2025.06.005
- VernacularTitle:HN-Seg:基于分支血管形态特征学习和噪声标签优化的肝脏血管分割方法
- Author:
Zheyuan ZHANG
1
;
Jisu HU
1
;
Bo PENG
1
;
Zhiyong ZHOU
1
;
Yakang DAI
1
Author Information
1. 中国科学技术大学生物医学工程学院(苏州)生命科学与医学部,江苏 苏州 215163;中国科学院苏州生物医学工程技术研究所,江苏 苏州 215163
- Publication Type:Journal Article
- Keywords:
hepatic vascular segmentation;
vascular morphology;
hierarchical vascular morphology aware network;
self-distillation noisy label refine
- From:
Chinese Journal of Medical Physics
2025;42(6):730-739
- CountryChina
- Language:Chinese
-
Abstract:
A novel approach named hierarchical vascular morphology awareness and noisy label refine for hepatic vessel segmentation(HN-Seg)is proposed to achieve precise vessel segmentation while reducing dependency on high-quality labels.HN-Seg comprises of(1)hierarchical vascular morphology aware network which employs a multi-scale local morphology attention mechanism and a global morphology preservation loss function to ensure the integrity of overall vascular morphology,and(2)self-distillation noisy label refine module which leverages the uncertainty in model outputs to optimize noisy labels through uncertainty optimization and consistency regularization,thereby maximizing the knowledge extracted from images during training and refining noisy labels.Experimental results on the hepatic vessel dataset demonstrate that HN-Seg achieves superior segmentation performance,outperforming 6 methods(UNet,UNet++,UNETR,SwinUNetR,FRUNet,and MTCL).HN-Seg attains DSC and clDice scores of 0.727 and 0.773,showing improvements of 9.6%and 21.5%over the baseline method UNETR.
