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.