Exploring Unlabeled Data in Multiple Aspects for Semi-Supervised MRI Segmentation

Background: MRI segmentation offers crucial insights for automatic analysis. Although deep learning-based segmentation methods have attained cutting-edge performance, their efficacy heavily relies on vast sets of meticulously annotated data. Methods: In this study, we propose a novel semi-supervised MRI segmentation model that is able to explore unlabeled data in multiple aspects based on various semi-supervised learning technologies. Results: We compared the performance of our proposed method with other deep learning-based methods on 2 public datasets, and the results demonstrated that we have achieved Dice scores of 90.3% and 89.4% on the LA and ACDC datasets, respectively. Conclusions: We explored the synergy of various semi-supervised learning technologies for MRI segmentation, and our investigation will inspire research that focuses on designing MRI segmentation models.