SESAME: Toward Medical Image Segmentation via Foundation Model-assisted Semi-supervised Learning

Medical image segmentation is essential for diagnosis but requires expensive and time-consuming labeled data. Semi-supervised learning (SSL) mitigates this issue by using unlabeled data to improve generalization. However, current SSL methods encounter issues with inadaptive perturbations and low-quality pseudo-labels. Vision foundation models, such as SAM, have shown promise in segmentation. We propose SESAME, an SSL method integrating SAM and U-Net to improve labeling accuracy through a foundation model-assisted pipeline. In particular, we introduce a reliability score to address low-quality pseudo-labels and employ strategies for utilizing both reliable and unreliable images. Reliable images are associated with refined pseudo-labels via a conflict resolving strategy, whereas unreliable ones undergo a mutual region swapping strategy. Extensive experiments demonstrate that our SESAME outperforms representative methods for medical image segmentation.