ProtoSAM-3D: Interactive semantic segmentation in volumetric medical imaging via a Segment Anything Model and mask-level prototypes.

Semantic segmentation of volumetric medical images is essential for accurate delineation of anatomic structures and pathology, enabling quantitative analysis in precision medicine applications. While volumetric segmentation has been extensively studied, most existing methods require full supervision and struggle to generalize to new classes at inference time, particularly for irregular, ill-defined targets such as tumors, where fine-grained, high-salience segmentation is required. Consequently, conventional semantic segmentation methods cannot easily offer zero/few-shot generalization to segment objects of interest beyond their closed training set.