Automatic segmentation of the heart cavity is an essential task for the diagnosis of cardiac diseases. In this paper, we propose a semi-supervised segmentation setup for leveraging unlabeled data to segment Left-ventricle, Right-ventricle, and Myocardium. We utilize an enhanced version of residual U-Net architecture on a large-scale cardiac MRI dataset. Handling the class imbalanced data issue using dice loss, the enhanced supervised model is able to achieve better dice scores in comparison with a vanilla U-Net model. We applied several augmentation techniques including histogram matching to increase the performance of our model in other domains. Also, we introduce a simple but efficient semi-supervised segmentation method to improve segmentation results without the need for large labeled data. Finally, we applied our method on two benchmark datasets, STACOM2018, and M\&Ms 2020 challenges, to show the potency of the proposed model. The effectiveness of our proposed model is demonstrated by the quantitative results. The model achieves average dice scores of 0.921, 0.926, and 0.891 for Left-ventricle, Right-ventricle, and Myocardium respectively.

翻译：心脏腔室的自动分割是诊断心脏疾病的关键任务。本文提出了一种半监督分割框架，通过利用未标注数据实现左心室、右心室和心肌的分割。我们采用增强型残差U-Net架构在大型心脏MRI数据集上进行处理。通过使用dice损失处理类别不平衡数据问题，增强型监督模型相较于原始U-Net模型获得了更高的dice系数。我们应用了包括直方图匹配在内的多种数据增强技术，以提升模型在其他域中的性能。同时，我们提出了一种简单高效且无需大量标注数据的半监督分割方法。最终，我们在STACOM2018和M&Ms 2020两个基准数据集上验证了所提模型的效能。定量结果证实了模型的有效性，其左心室、右心室和心肌的平均dice系数分别达到0.921、0.926和0.891。