CSCE: Cross Supervising and Confidence Enhancement pseudo-labels for semi-supervised subcortical brain structure segmentation

Robust and accurate segmentation of subcortical structures in brain MR images lays the foundation for observation, analysis and treatment planning of various brain diseases. Deep learning techniques based on Deep Neural Networks (DNNs) have achieved remarkable results in medical image segmentation by using abundant labeled data. However, due to the time-consuming and expensive of acquiring high quality annotations of brain subcortical structures, semi-supervised algorithms become practical in application. In this paper, we propose a novel framework for semi-supervised subcortical brain structure segmentation, based on pseudo-labels Cross Supervising and Confidence Enhancement (CSCE). Our framework comprises dual student-teacher models, specifically a U-Net and a TransUNet. For unlabeled data training, the TransUNet teacher generates pseudo-labels to supervise the U-Net student, while the U-Net teacher generates pseudo-labels to supervise the TransUNet student. This mutual supervision between the two models promotes and enhances their performance synergistically. We have designed two mechanisms to enhance the confidence of pseudo-labels to improve the reliability of cross-supervision: a) Using information entropy to describe uncertainty quantitatively; b) Design an auxiliary detection task to perform uncertainty detection on the pseudo-labels output by the teacher model, and then screened out reliable pseudo-labels for cross-supervision. Finally, we construct an end-to-end deep brain structure segmentation network only using one teacher network (U-Net or TransUNet) for inference, the segmentation results are significantly improved without increasing the parameters amount and segmentation time compared with supervised U-Net or TransUNet based segmentation algorithms. Comprehensive experiments are performed on two public benchmark brain MRI datasets. The proposed method achieves the best Dice scores and MHD values on both datasets compared to several recent state-of-the-art semi-supervised segmentation methods.