Correlation-based switching mean teacher for semi-supervised medical image segmentation

Abstract

The mean teacher framework is one of the mainstream approaches in semi-supervised medical image segmentation. While training together in the traditional mean teacher framework, the teacher model and the student model share the same structure. An Exponential Moving Average (EMA) updating strategy is applied to optimize the teacher model. Although the EMA approach facilitates a smooth training process, it causes the model coupling and error accumulation problems. These issues constrain the model from precisely delineating the regions of pathological structures, especially for the low-contrast regions in medical images. In this paper, we propose a new semi-supervised segmentation model, namely Correlation-based Switching Mean Teacher (CS-MT), which comprises two teacher models and one student model to alleviate these problems. Particularly, two teacher models adopt a switching training strategy at every epoch to avoid the convergence and similarity between the teacher models and the student model. In addition, we introduce a feature correlation module in each model to leverage the similarity information in the feature maps to improve the model’s predictions. Furthermore, the stochastic process of CutMix operation destroys the structures of organs in medical images, generating adverse mixed results. We propose an adaptive CutMix manner to mitigate the negative effects of these mixed results in model training. Extensive experiments validate that CS-MT outperforms the state-of-the-art semi-supervised methods on the LA, Pancreas-NIH, ACDC and BraTS 2019 datasets.