Semi-supervised medical image segmentation method based on consistency regularization
10.3969/j.issn.1005-202X.2025.06.011
- VernacularTitle:基于一致性正则化的半监督医学图像分割方法
- Author:
Xinhui XU
1
;
Zhiyong ZENG
;
Zhengyu LIN
Author Information
1. 福建师范大学计算机与网络安全学院,福建 福州 350117
- Publication Type:Journal Article
- Keywords:
semi-supervised medical image segmentation;
uncertainty estimation;
edge-preserving noise;
exponential moving average
- From:
Chinese Journal of Medical Physics
2025;42(6):784-790
- CountryChina
- Language:Chinese
-
Abstract:
In response to the high cost and time consumption of medical image annotation,and issues such as the imprecision of unlabeled data segmentation in semi-supervised medical image segmentation,loss of image edge information,and delayed parameter updates,a semi-supervised medical image segmentation method based on consistency regularization is presented.Firstly,an uncertainty measurement method based on the dual perspectives of entropy and variance is designed to assess the uncertainty of predictions for unlabeled data,jointly evaluating the uncertainty of unlabeled data from the perspectives of entropy and variance.Then,edge-preserving noise based on the Canny operator is used to retain image edge information and important structures,thereby avoiding the potential blurring of organ edges that may result from the addition of random noise.Finally,a semi-supervised residual-driven segmentation method based on the mean teacher framework is developed,with a Frobenius norm regularization term in the exponential moving average scheme to enhance the performance of mean teacher.The proposed method is validated on the publicly available multi-organ segmentation benchmark dataset BTCV and brain tumor segmentation dataset BraTS 2019.In the case of 40%labeled data in the BTCV dataset,Dice similarity coefficient and standardized surface distance are 77.42%and 79.47%,respectively.In the case of 20%labeled data in the BraTS 2019 dataset,the proposed method achieve a Dice similarity coefficient of 83.89%,a Jaccard coefficient of 74.21%,an average surface distance of 2.34 mm,and a 95%Hausdorff distance of 9.08 mm,demonstrating its superiority.
