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.
Guo et al. (Sat,) studied this question.
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