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

Dhirachaikulpanich, Dhanach ORCID: 0000-0003-2234-1837, Xie, Jianyang ORCID: 0000-0002-4565-5807, Chen, Xiuju, Li, Xiaoxin, Madhusudhan, Savita, Zheng, Yalin ORCID: 0000-0002-7873-0922 and Beare, Nicholas AV ORCID: 0000-0001-8086-990X (2024) Using Deep Learning to Segment Retinal Vascular Leakage and Occlusion in Retinal Vasculitis. Ocular immunology and inflammation, ahead- (ahead-). pp. 1-8.

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Official URL: http://dx.doi.org/10.1080/09273948.2024.2305185

Abstract

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. 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. 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). Our RV segmentation models could perform reliable segmentation for retinal vascular leakage and occlusion in FAs of RV patients.

Item Type:	Article
Uncontrolled Keywords:	Artificial intelligence, deep learning, fluorescein angiography, posterior uveitis, retinal vasculitis
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	25 Jan 2024 10:29
Last Modified:	15 Mar 2024 10:26
DOI:	10.1080/09273948.2024.2305185
Open Access URL:	https://doi.org/10.1080/09273948.2024.2305185
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3178020