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Early-stage diabetic retinopathy (DR) presents challenges in clinicaldiagnosis due to inconspicuous and minute microangioma lesions, resulting inlimited research in this area. Additionally, the potential of emergingfoundation models, such as the segment anything model (SAM), in medicalscenarios remains rarely explored. In this work, we propose ahuman-in-the-loop, label-free early DR diagnosis framework called GlanceSeg,based on SAM. GlanceSeg enables real-time segmentation of microangioma lesionsas ophthalmologists review fundus images. Our human-in-the-loop frameworkintegrates the ophthalmologist's gaze map, allowing for rough localization ofminute lesions in fundus images. Subsequently, a saliency map is generatedbased on the located region of interest, which provides prompt points to assistthe foundation model in efficiently segmenting microangioma lesions. Finally, adomain knowledge filter refines the segmentation of minute lesions. Weconducted experiments on two newly-built public datasets, i.e., IDRiD andRetinal-Lesions, and validated the feasibility and superiority of GlanceSegthrough visualized illustrations and quantitative measures. Additionally, wedemonstrated that GlanceSeg improves annotation efficiency for clinicians andenhances segmentation performance through fine-tuning using annotations. Thisstudy highlights the potential of GlanceSeg-based annotations for self-modeloptimization, leading to enduring performance advancements through continuallearning.

GlanceSeg: Real-time microaneurysm lesion segmentation with gaze-map-guided foundation model for early detection of diabetic retinopathy