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Automatic segmentation of the heart cavity is an essential task for thediagnosis of cardiac diseases. In this paper, we propose a semi-supervisedsegmentation setup for leveraging unlabeled data to segment Left-ventricle,Right-ventricle, and Myocardium. We utilize an enhanced version of residualU-Net architecture on a large-scale cardiac MRI dataset. Handling the classimbalanced data issue using dice loss, the enhanced supervised model is able toachieve better dice scores in comparison with a vanilla U-Net model. We appliedseveral augmentation techniques including histogram matching to increase theperformance of our model in other domains. Also, we introduce a simple butefficient semi-supervised segmentation method to improve segmentation resultswithout the need for large labeled data. Finally, we applied our method on twobenchmark datasets, STACOM2018, and M\&Ms 2020 challenges, to show the potencyof the proposed model. The effectiveness of our proposed model is demonstratedby the quantitative results. The model achieves average dice scores of 0.921,0.926, and 0.891 for Left-ventricle, Right-ventricle, and Myocardiumrespectively.

Semi-Supervised Segmentation of Multi-vendor and Multi-center Cardiac MRI using Histogram Matching