计算机工程与应用2025，Vol.61Issue(4)：253-261,9.DOI:10.3778/j.issn.1002-8331.2310-0062
交换标签部分和交叉监督的半监督医学图像分割
Semi-Supervised Medical Image Segmentation with Label-Part Switching and Cross-Teaching
摘要
Abstract
A semi-supervised medical image segmentation algorithm incorporating label-part switching and cross-teaching is proposed in response to the current challenges in the field of medical image segmentation,including low segmentation accuracy and high costs and difficulty in data acquisition.The label-part switching algorithm locates and exchanges the label portions of two images,addressing issues related to data distribution mismatch and empirical distribution gaps.The Transformer network is applied in the Mean Teachers architecture,employed for cross-teaching with CNN to assist in improving the quality of pseudo-label generation.A training strategy is introduced for images with swapped labels during pre-training and self-training,expanding the training dataset to enable the model to learn more features.In experiments with 10%labeled data on the ACDC dataset,the Dice coefficient reaches 90.67%,showing a 2.26 percentage points improvement over the baseline model.In experiments with 5%labeled data on the ACDC dataset and 20%labeled data on the PROMISE12 dataset,the Dice coefficients reach 88.69%and 84.34%,respectively.Comparative experiments with other methods demonstrate optimal performance across various metrics,validating the effectiveness and reliability of the proposed approach.关键词
医学图像分割/半监督/交换标签部分/交叉监督Key words
medical image segmentation/semi-supervised/label-part switching/cross-teaching分类
信息技术与安全科学引用本文复制引用
罗毅恒,张俊华,张剑青..交换标签部分和交叉监督的半监督医学图像分割[J].计算机工程与应用,2025,61(4):253-261,9.基金项目
国家自然科学基金(62063034,61841112). （62063034,61841112）
