计算机技术与发展2026，Vol.36Issue(9)：54-63,10.DOI:10.20165/j.cnki.ISSN1673-629X.2026.0066
面向医学图像分割的半监督自相似聚类方法
A Semi-supervised Self-similarity Clustering Method for Medical Image Segmentation
摘要
Abstract
To address the issue of insufficient model generalization caused by the scarcity of labeled data and high labeling costs in medi-cal image segmentation,we propose a novel semi-supervised multi-class self-similarity clustering framework(SMSC).Firstly,to fully leverage the inherent anatomical structure priors in unlabeled data,global features are dynamically divided into medial and lateral anatomi-cal subregions,enhancing the model's ability to represent local fine-grained features.Secondly,a multi-class self-similarity clustering module is proposed,which generates pseudo-labels for unlabeled data through unsupervised clustering and introduces a triplet loss con-straint to achieve intra-class compactness and inter-class separability in the feature space,mitigating model degradation under limited su-pervision.Finally,a pseudo-label collaborative training mechanism is constructed based on a clustering-guided strategy.Through joint optimization of a small amount of real annotations and a large number of high-quality pseudo-labels,effective propagation of labeling in-formation and steady improvement in model performance are achieved.SMSC significantly improves segmentation accuracy on knee MRI datasets(OAI-ZIB,OAI-Imorphics)and lung CT datasets(LUNA16),with Dice coefficients showing substantial improvement over the baseline nnU-Net model in multiple key structures,providing an effective solution for medical image segmentation under limited labeling resources.关键词
医学图像分割/半监督学习/域自适应/自相似聚类/伪标签优化Key words
medical image segmentation/semi-supervised learning/domain adaptation/self-similarity clustering/pseudo-label optimiza-tion分类
信息技术与安全科学引用本文复制引用
范睿,程远志..面向医学图像分割的半监督自相似聚类方法[J].计算机技术与发展,2026,36(9):54-63,10.基金项目
山东省自然科学基金(ZR2025MS1009) （ZR2025MS1009）
