北京生物医学工程2025，Vol.44Issue(3)：258-265,8.DOI:10.3969/j.issn.1002-3208.2025.03.006
基于对比学习和插值交叉一致性的半监督肺部血管分割算法
Semi-supervised lung vessel segmentation based on contrastive learning and interpolation cross-consistency
摘要
Abstract
Objective The segmentation of vessels in lung CT images helps in the diagnosis of diseases and provides an important reference for surgical navigation.For the current problems of scarcity of labeled data,complex morphology of blood vessels,and similar grayscale of pulmonary artery and pulmonary vein vessels in the task of lung blood vessel segmentation,we propose a Contrast learning based semi-supervised segmentation framework for automatic segmentation of pulmonary vessels.Assist the doctor in diagnosis.Methods The model combines contrast learning and semi-supervised learning,we design a non-parametric dynamic memory-based contrast learning strategy to efficiently achieve improve the similarity between intra-class features and to increase distances between inter-class features by comparing features in the memory bank.This method performs interpolation operations on unlabeled data and combines implicit shape awareness and cross-pseudo-supervision to construct consistency constraints.Finally,55 CT images from the open data set CARVE14 were used,and Dice coefficient was used as the main evaluation index to perform a comparative experiment on the segmentation performance of pulmonary arteriovenous vessels between Semi-CLIC and other 8 algorithms.Results Using two labeling ratios(10%and 20%)on the CARVE14 dataset,the average Dice scores of the proposed model were 69.4%and 71.4%,respectively,which were 1.5%and 0.9%higher than the best existing semi-supervised algorithm.Conclusions Semi-supervised learning can obtain generalization performance similar to that of fully supervised learning when only a small amount of labeled data is used,which is an effective method to alleviate the scarcity of medical image labeled data.关键词
半监督分割/对比学习/血管分割/形状感知/插值交叉一致性Key words
semi-supervised segmentation/contrastive learning/vessel segmentation/shape awareness/interpolation cross-consistency分类
医药卫生引用本文复制引用
张昱中,周威,罗晶,周雷..基于对比学习和插值交叉一致性的半监督肺部血管分割算法[J].北京生物医学工程,2025,44(3):258-265,8.基金项目
国家自然科学基金(61906121)资助 （61906121）
