Semi-supervised cardiac magnetic resonance image segmentation based on domain generalization  

作　　者：SHAO Hong HOU Jinyang CUI Wencheng 邵虹

机构地区：[1]School of Information Science and Engineering,Shenyang University of Technology,Shenyang 110870,P.R.China

出　　处：《High Technology Letters》2025年第1期41-52,共12页高技术通讯(英文版)

基　　金：Supported by the National Natural Science Foundation of China(No.62001313);the Key Project of Liaoning Provincial Department of Science and Technology(No.2021JH2/10300134,2022JH1/10500004)。

摘　　要：In the realm of medical image segmentation,particularly in cardiac magnetic resonance imaging(MRI),achieving robust performance with limited annotated data is a significant challenge.Performance often degrades when faced with testing scenarios from unknown domains.To address this problem,this paper proposes a novel semi-supervised approach for cardiac magnetic resonance image segmentation,aiming to enhance predictive capabilities and domain generalization(DG).This paper establishes an MT-like model utilizing pseudo-labeling and consistency regularization from semi-supervised learning,and integrates uncertainty estimation to improve the accuracy of pseudo-labels.Additionally,to tackle the challenge of domain generalization,a data manipulation strategy is introduced,extracting spatial and content-related information from images across different domains,enriching the dataset with a multi-domain perspective.This papers method is meticulously evaluated on the publicly available cardiac magnetic resonance imaging dataset M&Ms,validating its effectiveness.Comparative analyses against various methods highlight the out-standing performance of this papers approach,demonstrating its capability to segment cardiac magnetic resonance images in previously unseen domains even with limited annotated data.

关 键 词：SEMI-SUPERVISED domain generalization(DG) cardiac magnetic resonance image segmentation 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]