Semi-supervised cardiac magnetic resonance image segmentation based on domain generalization
简介：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.Per-formance 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 im-age 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-la-bels.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 paper·s method is meticulously evaluated on the publicly available cardiac magnetic resonance imaging dataset M&Ms,validating its effective-ness.Comparative analyses against various methods highlight the out-standing performance of this paper·s approach,demonstrating its capability to segment cardiac magnetic resonance images in pre-viously unseen domains even with limited annotated data.展开
学者：SHAOHONGHouJinyangCuiWencheng
关键词：semi-superviseddomain generalization(DG)cardiac magnetic resonanceim-age segmentation
在线出版日期：2025-04-21 （网站首发日期）
