OCT Signal Enhancement with Deep Learning

Lazaridis, Georgios and Lorenzi, Marco and Mohamed-Noriega, Jibran and Aguilar-Munoa, Soledad and Suzuki, Katsuyoshi and Nomoto, Hiroki and Ourselin, Sebastien and Garway-Heath, David F. and Crabb, David P. and Bunce, Catey and Amalfitano, Francesca and Anand, Nitin and Azuara-Blanco, Augusto and Bourne, Rupert R. A. and Broadway, David C. and Cunliffe, Ian A. and Diamond, Jeremy P. and Fraser, Scott G. and Ho, Tuan A. and Martin, Keith R. and McNaught, Andrew I. and Negi, Anil and Shah, Ameet and Spry, Paul G. and White, Edward T. and Wormald, Richard P. and Xing, Wen and Zeyen, Thierry G. (2021) OCT Signal Enhancement with Deep Learning. Ophthalmology Glaucoma, 4 (3). pp. 295-304. ISSN 2589-4196

Full text not available from this repository.
Official URL: https://doi.org/10.1016/j.ogla.2020.10.008


Purpose- To establish whether deep learning methods are able to improve the signal-to-noise ratio of time-domain (TD) OCT images to approach that of spectral-domain (SD) OCT images. Design- Method agreement study and progression detection in a randomized, double-masked, placebo-controlled, multicenter trial for open-angle glaucoma (OAG), the United Kingdom Glaucoma Treatment Study (UKGTS). Participants- The training and validation cohort comprised 77 stable OAG participants with TD OCT and SD OCT imaging at up to 11 visits within 3 months. The testing cohort comprised 284 newly diagnosed OAG patients with TD OCT images from a cohort of 516 recruited at 10 United Kingdom centers between 2007 and 2010. Methods- An ensemble of generative adversarial networks (GANs) was trained on TD OCT and SD OCT image pairs from the training dataset and applied to TD OCT images from the testing dataset. Time-domain OCT images were converted to synthesized SD OCT images and segmented via Bayesian fusion on the output of the GANs. Main Outcome Measures- Bland-Altman analysis assessed agreement between TD OCT and synthesized SD OCT average retinal nerve fiber layer thickness (RNFLT) measurements and the SD OCT RNFLT. Analysis of the distribution of the rates of RNFLT change in TD OCT and synthesized SD OCT in the two treatment arms of the UKGTS was compared. A Cox model for predictors of time-to-incident visual field (VF) progression was computed with the TD OCT and the synthesized SD OCT images. Results- The 95% limits of agreement were between TD OCT and SD OCT were 26.64 to –22.95; between synthesized SD OCT and SD OCT were 8.11 to –6.73; and between SD OCT and SD OCT were 4.16 to –4.04. The mean difference in the rate of RNFLT change between UKGTS treatment and placebo arms with TD OCT was 0.24 (P = 0.11) and with synthesized SD OCT was 0.43 (P = 0.0017). The hazard ratio for RNFLT slope in Cox regression modeling for time to incident VF progression was 1.09 (95% confidence interval [CI], 1.02–1.21; P = 0.035) for TD OCT and 1.24 (95% CI, 1.08–1.39; P = 0.011) for synthesized SD OCT. Conclusions- Image enhancement significantly improved the agreement of TD OCT RNFLT measurements with SD OCT RNFLT measurements. The difference, and its significance, in rates of RNFLT change in the UKGTS treatment arms was enhanced and RNFLT change became a stronger predictor of VF progression.

Item Type: Journal Article
Additional Information: A COPY IS AVAILABLE AT: https://discovery.ucl.ac.uk/id/eprint/10115263/
Keywords: Deep learning, Glaucoma, Image analysis, OCT, Visual fields
Faculty: Faculty of Health, Education, Medicine & Social Care
Depositing User: Lisa Blanshard
Date Deposited: 14 Dec 2021 14:18
Last Modified: 24 Jan 2022 16:10
URI: https://arro.anglia.ac.uk/id/eprint/707174

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