Zero-Shot Capillary Segmentation in Dermoscopy Images via SAM2: A Case Study on Oral Mucosa

Morphological changes in oral mucosal microvasculature serve as early diagnostic markers for various diseases. However, existing dermoscopy image analysis relies heavily on physician expertise, leading to high subjectivity and low efficiency. This paper proposes a zero-shot capillary segmentation method for oral mucosa based on Segment Anything Model 2 (SAM2), which effectively handles reflection artifacts and highlights minute vascular structures through a multi-scale adaptive enhancement algorithm. The method employs a morphology-aware automatic prompt annotation strategy to generate composite guidance containing bounding boxes, foreground points, and background points for SAM2. Without requiring annotated data or model training, this approach achieves precise instance segmentation of capillaries through an "enhancement-annotation-segmentation" collaborative paradigm. On a clinical dataset comprising 212 dermoscopy images from 106 subjects, our method achieved a Dice coefficient of 0.7278 and an IoU of 0.5721, representing improvements of 17.12% and 26.9% respectively compared to the medical-specific baseline MedSAM. This provides an objective auxiliary diagnostic method for oral mucosal diseases that depend on capillary morphology analysis.