DSST: A dual student model guided student-teacher framework for semi-supervised medical image segmentation

Numerous semi-supervised learning methods are emerging in medical image segmentation to reduce the dependency of deep learning models on pixel-level annotation data. Consistency regularization methods based on the Student-Teacher structure have achieved brilliant results in this domain. However, the current structures are unable to resolve the tight weight coupling satisfactorily between the teacher and student model, which leads to a decrease in the segmentation performance. In this paper, we propose a novel and practical semi-supervised learning framework, Dual-Student-Single-Teacher (DSST), to alleviate this problem. Particularly, the DSST framework consists of three segmentation models with the identical structure but different initial parameters, one serves as the teacher model and others as the student models, which employs an alternating manner to update the teacher model parameters. For the DSST framework, we present different supervised modes to sufficiently explore the enhancement of consistency regularization for model segmentation performance. Furthermore, we also introduce abundant and efficient input and feature perturbations for the proposed method. Finally, we evaluate our framework in three public medical image segmentation tasks, including Pancreas-CT, LA dataset, and cardiac segmentation on the ACDC dataset. Extensive experiments demonstrate that, compared with eight other superior semi-supervised methods, the DSST method obtains state-of-the-art segmentation performance and is an effective and generalizable framework. Code is available at https://github.com/LBL0704/DSST.