MicroLesion-SAM: Centerline-Guided Foundation Model for Small Lesion Segmentation

Detecting small and low-contrast brain lesions is difficult for conventional foundation models, which often prioritize larger structures and overlook micro-lesions. MicroLesion-SAM enhances SAM by integrating structural centerline priors and scale-adaptive refinement to increase sensitivity to lesions below 20 voxels. The model uses probabilistic skeleton cues to guide attention toward lesion cores while preserving high-frequency features through resolution-adaptive enhancement. Evaluated on WMH2020 (60 subjects) and ISLES2018 (103 subjects), MicroLesion-SAM increases small-lesion recall from 0.684 to 0.812 (+18.7%) and raises global Dice to 0.902 (+4.5% over SAM-Med2D). HD95 decreases from 21.9 mm to 14.6 mm (−33.3%), and lesion-wise F1 improves by 11.2%. Cross-dataset validation on an independent clinical cohort of 72 subjects shows a 12.1% improvement in micro-lesion detection. Ablation experiments confirm that the centerline priors contribute a 7.3% Dice gain, while heatmap analysis shows more accurate localization of micro-vascular abnormalities.