Mutually aided uncertainty incorporated dual consistency regularization with pseudo label for semi-supervised medical image segmentation

Semi-supervised learning has contributed plenty to promoting computer vision tasks. Especially concern-ing medical images, semi-supervised image segmentation can significantly reduce the labor and time cost of labeling images. Among the existing semi-supervised methods, pseudo-labelling and consistency reg-ularization prevail; however, the current related methods still need to achieve satisfactory results due to the poor quality of the pseudo-labels generated and needing more certainty awareness the models. To address this problem, we propose a novel method that combines pseudo-labelling with dual consistency regularization based on a high capability of uncertainty awareness. This method leverages a cycle-loss regularized to lead to a more accurate uncertainty estimate. Followed by the uncertainty estimation, the certain region with its pseudo-label is further trained in a supervised manner. In contrast, the uncer-tain region is used to promote the dual consistency between the student and teacher networks. The developed approach was tested on three public datasets and showed that: 1) The proposed method achieves excellent performance improvement by leveraging unlabeled data; 2) Compared with several state-of-the-art (SOTA) semi-supervised segmentation methods, ours achieved better or comparable performance.& COPY; 2023 Elsevier B.V. All rights reserved.

Automated summary

Semi-supervised learning has played a significant role in advancing computer vision tasks, particularly in the field of medical images. This study introduces a novel approach that combines pseudo-labelling with dual consistency regularization to improve segmentation results. By leveraging the uncertainty awareness capability, the proposed method achieves better performance by accurately estimating uncertainty and promoting consistency between networks. Experimental results on three public datasets demonstrate the effectiveness and superiority of the proposed method compared to state-of-the-art approaches.