A Domain-Free Semi-supervised Method for Myocardium Segmentation in 2D Echocardiography Sequences

Many deep learning methods have been applied in myocardium segmentation, however, the robustness of these algorithms is relatively low, especially when dealing with datasets from different domains, such as machines. In this paper, we propose a domain-free semi-supervised deep learning algorithm to improve the model robustness between different machines. Two domain-free factors (the shape of the myocardium and the motion tendency between adjacent frames) are adopted. Specifically, an optical flow field-based segmentation network is proposed for enhancing the performance by combining the motion tendency of myocardium between adjacent frames. Moreover, a shape-based semi-supervised adversarial network is presented to utilize the shape of the myocardium for the purposes of improving the segmentation robustness. Experiments on our private and public datasets show that the proposed method not only improves the segmentation performance, but also decreases the performance gap when applied to different machines, thus demonstrating the effectiveness of the proposed method.