Tripled-Uncertainty Guided Mean Teacher Model for Semi-supervised Medical Image Segmentation

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 leverage 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 teacher model to generate more reliable pseudo labels. When calculating uncertainty, we propose an uncertainty weighted integration (UWI) strategy for yielding the segmentation predictions of the teacher. Extensive experiments on public 2017 ACDC dataset and PROMISE12 dataset have demostrated the effectiveness of our method. Code is available at https://github.com/DeepMedLab/Tri-U-MT.

Automated summary

This paper proposes a semi-supervised learning method that utilizes auxiliary tasks and task-level consistency to improve medical image segmentation using unlabeled data. By introducing two auxiliary tasks and a teacher model, it successfully overcomes the issue of scarce annotated data.