Uncertainty-aware consistency learning for semi-supervised medical image segmentation

Semi-supervised medical image segmentation faces two challenging issues: (1) insufficient exploration of latent structures leading to difficulty in comprehensively capturing complex features and structures in medical images; (2) sensitivity to noise, where unlabeled data lacks accurate label information, making the model more prone to noise interference during the learning process. In this paper, a method, uncertainty-aware consistency learning (UAC), is proposed to improve the poor generalization and suboptimal performance in semi-supervised medical image segmentation caused by insufficient information exploration and sensitivity to noise. Firstly, by employing multiple perturbation strategies at both the input and output levels, specifically through data-level and scale-level perturbations, the model is better equipped to capture structural information within organs and essential features that impact segmentation performance. Secondly, the perturbation uncertainty leverages perturbation prediction differences to measure uncertainty helps the model generate reliable predictions and avoid excessive focus on unreliable areas in the predictions. Experimental results on three public medical image segmentation datasets demonstrate that our UAC, utilizing multiple perturbation strategies and uncertainty estimation, exhibits generality across various organ segmentation tasks and achieves accurate segmentation, with the DICE of 91.15%(LA), 77.52%(Pancreas-CT) and 78.71%(PARSE) under a 10% label ratio setting. Comparative and ablation studies indicate that our method outperforms state-of-the-art semi-supervised medical image segmentation methods.