LDTSF: A LABEL-DECOUPLING TEACHER-STUDENT FRAMEWORK FOR SEMI-SUPERVISED ECHOCARDIOGRAPHY SEGMENTATION

The accurate segmentation of the right and left ventricles with limited labeled data is a challenging task in echocardiographic data analysis. To fully leverage the easily accessible unlabeled data, we propose a label-decoupling teacher-student framework (LDTSF) based on semi-supervised learning. Specifically, the decoupled deep network within LDTSF jointly predicts pixel-wise segmentation maps, level set-based edge regression maps, target skeleton maps and target detail maps to focus on edge pixels. Several micro-task-transformable layers are used to map multi-task representations to a unified space in order to supervise the consistency among multiple tasks using massive unlabeled data. In addition, we first train a teacher model based on semi-supervised learning strategy, and then use the pseudo-labels generated by the teacher model together with the original labels to train a student model. Experiments on our self-collected 3D echocardiographic dataset and a publicly available MRI dataset show that our method outperforms state-of-the-art semi-supervised learning methods. The code will be available at: https://github.com/SwanKnightZJP/LDTSF.