FM-ABS: Promptable Foundation Model Drives Active Barely Supervised Learning for 3D Medical Image Segmentation

Semi-supervised learning (SSL) has significantly advanced 3D medical image segmentation by effectively reducing the need for laborious dense labeling from radiologists. Traditionally focused on model-centric advancements, we anticipate that the SSL landscape will shift due to the emergence of open-source generalist foundation models, e.g., Segment Anything Model (SAM). These generalists have shown remarkable zero-shot segmentation capabilities with manual prompts, allowing a promising data-centric perspective for future SSL, particularly in pseudo and expert labeling strategies for enhancing the data pool. To this end, we propose the Foundation Model-driven Active Barely Supervised (FM-ABS) learning paradigm for developing customized 3D specialist segmentation models with shoestring annotation budgets, i.e., merely labeling three slices per scan. Specifically, building upon the basic mean-teacher framework, FM-ABS accounts for the intrinsic characteristics of 3D imaging and modernizes the SSL paradigm with two key data-centric designs: (i) specialist-generalist collaboration where the in-training specialist model delivers class-specific prompts to interact with the frozen class-agnostic generalist model across multiple views to acquire noisy-yet-effective pseudo labels, and (ii) expert-model collaboration that advocates active cross-labeling with notably low annotation efforts to progressively provide the specialist model with informative and efficient supervision in a human-in-the-loop manner, which benefits the automatic object-specific prompt generation in turn. Extensive experiments on two benchmark datasets show the promising results of our approach over recent SSL methods under extremely limited (barely) labeling budgets.