Push the Boundary of SAM: A Pseudo-label Correction Framework for Medical Segmentation

Segment anything model (SAM) has emerged as the leading approach for zero-shot learning in segmentation, offering the advantage of avoiding pixel-wise annotation. It is particularly appealing in medical image segmentation where annotation is laborious and expertise-demanding. However, the direct application of SAM often yields inferior results compared to conventional fully supervised segmentation networks. While using SAM generated pseudo label could also benefit the training of fully supervised segmentation, the performance is limited by the quality of pseudo labels. In this paper, we propose a novel label corruption to push the boundary of SAM-based segmentation. Our model utilizes a novel noise detection module to distinguish between noisy labels from clean labels. This enables us to correct the noisy labels using an uncertainty-based self-correction module, thereby enriching the clean training set. Finally, we retrain the network with updated labels to optimize its weights for future predictions. One key advantage of our model is its ability to train deep networks using SAM-generated pseudo labels without relying on a subset of expert-level annotations. We demonstrate the effectiveness of our proposed model on both X-ray and lung CT datasets, indicating its ability to improve segmentation accuracy and outperform baseline methods in label correction.