AutoPromptSeg: Automated Decoupling of Uncertainty Prompts with SAM for semi-supervised medical image segmentation

The scarcity of high-quality annotated data limits the application of supervised learning in disease diagnosis. Semi-supervised learning (SSL) offers a promising solution to this challenge, utilizing both limited labeled data and large-scale unlabeled data to significantly boost segmentation accuracy. While existing SSL methods focus on model-centric regularization strategies, the emergence of promptable foundation models like Segment Anything (SAM) presents new opportunities for paradigmatic advancement. However, SAM requires datasets with additional prompt annotations to guide the segmentation process, yet most existing medical imaging datasets do not contain them. To address this limitation, we introduce a novel semi-supervised 3D medical image segmentation method called AutoPromptSeg, which generates effective prompts with the Decoupled Uncertainty Prompt Generator (DUPG) while maintaining superior segmentation performance in data-scarce scenarios. Concurrently, we employ the Channel Alignment and Fusion Architecture (CAFA) to align features obtained from different branches, thereby bolstering the representational capacity of unlabeled data. Our proposed approach achieves state-of-the-art performance on three benchmarks: the multi-modality abdominal multi-organ segmentation challenge 2022 dataset (Amos 2022), the left atrium dataset (LA), and the brain tumor segmentation challenge 2020 dataset (BraTS 2020). AutoPromptSeg achieves Dice Score of 68.78% on Amos 2022, 90.02% on LA, and 86.63% on BraTS 2020 under only 10% labeled data setting, demonstrating the excellent performance of our semi-supervised learning framework in limited annotated data.