| SHAO Hong(邵 虹),HOU Jinyang,CUI Wencheng.[J].高技术通讯(英文),2025,31(1):41~52 |
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| Semi-supervised cardiac magnetic resonance image segmentation based on domain generalization |
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| DOI：10. 3772 / j. issn. 1006-6748. 2025. 01. 005 |
| 中文关键词: |
| 英文关键词: semi-supervised, domain generalization (DG), cardiac magnetic resonance, image segmentation |
| 基金项目: |
| Author Name | Affiliation | | SHAO Hong(邵 虹) | (School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, P. R. China) | | HOU Jinyang | | | CUI Wencheng | |
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| 中文摘要: |
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| 英文摘要: |
| 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 paper’s 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 paper’s approach, demonstrating its capability to segment cardiac magnetic resonance images in previously unseen domains even with limited annotated data. |
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