M4S-Net: a motion-enhanced shape-aware semi-supervised network for echocardiography sequence segmentation

Sequence segmentation of echocardiograms is of great significance for the diagnosis and treatment of cardiovascular diseases. However, the low quality of ultrasound imaging and the complexity of cardiac motion pose great challenges to it. In addition, the difficulty and cost of labeling echocardiography sequences limit the performance of supervised learning methods. In this paper, we proposed a Motion-enhanced Shape-aware Semi-supervised Sequence Segmentation Network named M4S-Net. First, multi-level shape priors are used to enhance the model's shape representation capabilities, overcoming the low image quality and improving single-frame segmentation. Then, a motion-enhanced optimization module utilizes optical flows to assist segmentation in a geometric sense, which robustly responds to the complex motions and ensures the temporal consistency of sequence segmentation. A hybrid loss function is devised to maximize the effectiveness of each module and further improve the temporal stability of predicted masks. Furthermore, the parameter-sharing strategy allows it to perform sequence segmentation in a semi-supervised manner. Massive experiments on both public and in-house datasets show that M4S-Net outperforms the state-of-the-art methods in both spatial and temporal segmentation performance. A downstream apical rocking recognition task based on M4S-Net also achieves an AUC of 0.944, which significantly exceeds specialized physicians.