Using Deep Learning to Segment Retinal Vascular Leakage and Occlusion in Retinal Vasculitis

Abstract

Purpose: Retinal vasculitis (RV) is characterised by retinal vascular leakage, occlusion or both on fluorescein angiography (FA). There is no standard scheme available to segment RV features. We aimed to develop a deep learning model to segment both vascular leakage and occlusion in RV. Methods: Four hundred and sixty-three FA images from 82 patients with retinal vasculitis were used to develop a deep learning model, in 60:20:20 ratio for training:validation:testing. Parameters, including deep learning architectures (DeeplabV3+, UNet++ and UNet), were altered to find the best binary segmentation model separately for retinal vascular leakage and occlusion, using a Dice score to determine the reliability of each model. Results: Our best model for vascular leakage had a Dice score of 0.6279 (95% confidence interval (CI) 0.5584–0.6974). For occlusion, the best model achieved a Dice score of 0.6992 (95% CI 0.6109–0.7874). Conclusion: Our RV segmentation models could perform reliable segmentation for retinal vascular leakage and occlusion in FAs of RV patients.