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

作　　者：罗毅恒 张俊华[1] 张剑青[2] LUO Yiheng;ZHANG Junhua;ZHANG Jianqing(School of Information,Yunnan University,Kunming 650504,China;Department of Respiratory and Critical Care Medicine,The 1st Affiliated Hospital of Kunming Medical University,Kunming 650032,China)

机构地区：[1]云南大学信息学院,昆明650504 [2]昆明医科大学第一附属医院呼吸与危重症医学二科,昆明650032

出　　处：《计算机工程与应用》2025年第4期253-261,共9页Computer Engineering and Applications

基　　金：国家自然科学基金(62063034,61841112)。

摘　　要：针对目前医学图像分割领域存在的分割精度不高和数据获取成本高、难度大的问题,提出了交换标签部分和交叉监督的半监督医学图像分割算法。交换标签部分算法定位两张图片的标签部分并进行交换,解决数据分布不匹配和经验分布差距问题。将Transformer网络应用在Mean Teachers架构,用于与CNN进行交叉监督,辅助CNN提高伪标签的生成质量。添加在预训练和自训练中加入交换标签后图像的训练策略,扩充训练数据集,使模型能学习到更多特征。在ACDC数据集10%有标签实验中Dice系数达到90.67%,较基准模型提升了2.26个百分点,在ACDC数据集5%有标签实验和PROMISE12数据集20%有标签实验中Dice系数分别达到88.69%和84.34%。与其他方法相比,多个实验各项指标均达到最优,实验结果证明了提出方法的有效性和可靠性。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 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]