Efficient cardiac MRI multi-structure segmentation for cardiovascular assessment with limited annotation by integrating data-level and network-level consistency.
Authors
Affiliations (7)
Affiliations (7)
- Department of Cardiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.
- Department of Cardiovascular Medicine, The Second Affiliated Hospital, University of South China, Hengyang, Hunan, China.
- Hengyang Medical School, University of South China, Hengyang, Hunan, China.
- Department of Sonography, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
- School of Computer Science & Artificial Intelligence, SR University, Warangal, Telangana, India. [email protected].
- Department of Cardiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China. [email protected].
- Department of Cardiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China. [email protected].
Abstract
Accurate segmentation of anatomical structures in cardiac magnetic resonance imaging (MRI) plays an irreplaceable role in the clinical management of cardiovascular diseases, serving as a cornerstone for precise diagnosis, individualized treatment planning, and long-term prognosis assessment. Although deep learning techniques have demonstrated promising performance in achieving automatic segmentation of cardiac MRI anatomical structures, their heavy reliance on large-scale labeled datasets for model training presents notable challenges in the field of cardiac imaging, as the annotations can only be provided by medical specialists with extensive experience. Against this backdrop, this work proposes a mutual ensemble framework integrating data-level and network-level consistency for semi-supervised learning to utilize limited labeled and abundant unlabeled data. Extensive experiments demonstrate that our approach can successfully harness unlabeled data to improve performance, outperforming existing segmentation methods under the same conditions.