Deep learning for retinal non-perfusion and foveal avascular zone analysis in wide-field OCTA in diabetic retinopathy.

ABSTRACT: We developed an automated framework for segmenting low-quality and non-perfusion areas in widefield OCTA images to obtain two key metrics useful for diabetic retinopathy (DR) monitoring: the retinal non-perfusion index (NPI) and foveal avascular zone (FAZ) area. Using 170 images from 88 patients in the EVIRED cohort, we trained two models: Q-NET, which segments low-quality areas, and NPA-NET, which detects non-perfusion areas and the FAZ. Their combined outputs created a 4-class map to calculate NPI and FAZ area. Ground truth segmentations were established by a single expert (for non-perfusion and FAZ areas) or a consensus of four annotators (for low-quality areas). NPA-NET and Q-NET, tested on 29 images, achieved strong segmentation performances (Dice coefficients of 0.714 (low-quality),

SUBMITTER: Le Boite H 

PROVIDER: S-EPMC12361365 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature