Semi-supervised medical image segmentation via a tripled-uncertainty guided mean teacher model with contrastive learning

Due to the difficulty in accessing a large amount of labeled data, semi-supervised learning is becoming an attractive solution in medical image segmentation. To make use of unlabeled data, current popular semi-supervised methods (e.g., temporal ensembling, mean teacher) mainly impose data-level and model-level consistency on unlabeled data. In this paper, we argue that in addition to these strategies, we could further utilize auxiliary tasks and consider task-level consistency to better excavate effective representations from unlabeled data for segmentation. Specifically, we introduce two auxiliary tasks, i.e., a foreground and background reconstruction task for capturing semantic information and a signed distance field (SDF) prediction task for imposing shape constraint, and explore the mutual promotion effect between the two auxiliary and the segmentation tasks based on mean teacher architecture. Moreover, to handle the potential bias of the teacher model caused by annotation scarcity, we develop a tripled-uncertainty guided framework to encourage the three tasks in the student model to learn more reliable knowledge from the teacher. When calculating uncertainty, we propose an uncertainty weighted integration (UWI) strategy for yielding the segmentation predictions of the teacher. In addition, following the advance of unsupervised learning in leveraging the unlabeled data, we also incorporate a contrastive learning based constraint to help the encoders extract more distinct representations to promote the medical image segmentation performance. Extensive experiments on the public 2017 ACDC dataset and the PROMISE12 dataset have demonstrated the effectiveness of our method.