Semi-supervised semantic segmentation method for glomerular ultrastructure
10.3969/j.issn.1005-202X.2025.06.008
- VernacularTitle:肾小球超微结构的半监督语义分割方法
- Author:
Xiang CHEN
1
;
Zhentai ZHANG
;
Kaixing LONG
;
Yanmeng LU
;
Jian GENG
;
Zhitao ZHOU
;
Lei CAO
Author Information
1. 南方医科大学生物医学工程学院,广东 广州 510515;广东省医学图像处理重点实验室,广东 广州 510515;广东省医学成像与诊断技术工程实验室,广东 广州 510515
- Publication Type:Journal Article
- Keywords:
medical image segmentation;
semi-supervised learning;
consistency regularization;
glomerular ultrastructure
- From:
Chinese Journal of Medical Physics
2025;42(6):757-765
- CountryChina
- Language:Chinese
-
Abstract:
Accurate identification of the glomerular ultrastructure is critical for the diagnosis of chronic kidney diseases,but the high cost of acquiring high-quality annotated data limits the application of fully-supervised learning.Therefore,a multi-class semi-supervised semantic segmentation framework based on segment anything model(MC4S-SAM)is proposed.After improving the mask decoder of segment anything model to enable multi-class semantic segmentation without requiring prompt information,the improved model is used to generate and refine pseudo-labels through a self-training strategy,and multi-level consistency regularization constraints are incorporated to enhance the model's performance.Experimental results show that,in the task of segmenting the glomerular mesangial ultrastructure,MC4S-SAM outperformes the fully-supervised model by 11.72%in mean intersection over union(mIoU)and 11.45%in mean Dice similarity coefficient(mDSC)when the labeled data accountes for 1/16 of the total.When the labeled data proportion is 1/4,the mIoU and mDSC reach 68.91%and 78.73%,respectively,demonstrating its significant potential for aiding the diagnosis of chronic kidney diseases.
