Semi-supervised TEE Segmentation via Interacting with SAM Equipped with Noise-Resilient Prompting

Abstract

Semi-supervised learning (SSL) is a powerful tool to address the challenge of insufficient annotated data in medical segmentation problems. However, existing semi-supervised methods mainly rely on internal knowledge for pseudo labeling, which is biased due to the distribution mismatch between the highly imbalanced labeled and unlabeled data. Segmenting left atrial appendage (LAA) from transesophageal echocardiogram (TEE) images is a typical medical image segmentation task featured by scarcity of professional annotations and diverse data distributions, for which existing SSL models cannot achieve satisfactory performance. In this paper, we propose a novel strategy to mitigate the inherent challenge of distribution mismatch in SSL by, for the first time, incorporating a large foundation model (i.e. SAM in our implementation) into an SSL model to improve the quality of pseudo labels. We further propose a new self-reconstruction mechanism to generate both noise-resilient prompts to demonically improve SAM’s generalization capability over TEE images and self-perturbations to stabilize the training process and reduce the impact of noisy labels. We conduct extensive experiments on an in-house TEE dataset; experimental results demonstrate that our method achieves better performance than state-of-the-art SSL models.