UP-SAM: Uncertainty-Informed Adaptation of Segment Anything Model for Semi-Supervised Medical Image Segmentation

Semi-supervised segmentation is extensively employed in medical image analysis due to its ability to leverage a small amount of labeled data alongside abundant unlabeled data. However, its performance is hindered by the inadequate knowledge of the data domain learned from limited labeled data and the absence of effective strategies for exploiting unlabeled regions, especially when annotations are extremely scarce. To address these challenges, the Segment Anything Model (SAM) has emerged as a promising solution. As a foundation model enriched by extensive and diverse domain knowledge, SAM has been leveraged to mitigate the epistemic uncertainty (EU) of semi-supervised segmentation models, while aleatoric uncertainty (AU) is often ignored. In this paper, we propose a novel semi-supervised medical image segmentation framework called UP-SAM, which adapts SAM for dual uncertainty assessments. The framework achieves effective collaboration between large foundation models and domain-specific models, leading to a simultaneous reduction in the impact of EU and AU. The experiments on the left atrium and pancreas datasets demonstrate the superior efficacy of UP-SAM against baseline methods. Particularly, UP-SAM exhibits substantial advantages over other semi-supervised learning models when dealing with exceedingly scarce labeled data. Code is available at https://github.com/VivienLu/UP-SAM.