A deep learning model for generating fundus autofluorescence images from color fundus photography

Fan Song; Weiyi Zhang; Yingfeng Zheng; Danli Shi; Mingguang He

doi:10.1016/j.aopr.2023.11.001

A deep learning model for generating fundus autofluorescence images from color fundus photography

Adv Ophthalmol Pract Res. 2023 Nov 10;3(4):192-198. doi: 10.1016/j.aopr.2023.11.001. eCollection 2023 Nov-Dec.

Authors

Fan Song^{1

2}, Weiyi Zhang^{1

2}, Yingfeng Zheng³, Danli Shi^{1

2}, Mingguang He^{1

2}

Affiliations

¹ Experimental Ophthalmology, School of Optometry, The Hong Kong Polytechnic University, Hong Kong, China.
² Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Hong Kong, China.
³ State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.

Abstract

Background: Fundus Autofluorescence (FAF) is a valuable imaging technique used to assess metabolic alterations in the retinal pigment epithelium (RPE) associated with various age-related and disease-related changes. The practical uses of FAF are ever-growing. This study aimed to evaluate the effectiveness of a generative deep learning (DL) model in translating color fundus (CF) images into synthetic FAF images and explore its potential for enhancing screening of age-related macular degeneration (AMD).

Methods: A generative adversarial network (GAN) model was trained on pairs of CF and FAF images to generate synthetic FAF images. The quality of synthesized FAF images was assessed objectively by common generation metrics. Additionally, the clinical effectiveness of the generated FAF images in AMD classification was evaluated by measuring the area under the curve (AUC), using the LabelMe dataset.

Results: A total of 8410 FAF images from 2586 patients were analyzed. The synthesized FAF images exhibited an impressive objectively assessed quality, achieving a multi-scale structural similarity index (MS-SSIM) of 0.67. When evaluated on the LabelMe dataset, the combination of generated FAF images and CF images resulted in a noteworthy improvement in AMD classification accuracy, with the AUC increasing from 0.931 to 0.968.

Conclusions: This study presents the first attempt to use a generative deep learning model to create authentic and high-quality FAF images from CF images. The incorporation of the translated FAF images on top of CF images improved the accuracy of AMD classification. Overall, this study presents a promising approach to enhance large-scale AMD screening.

Keywords: Age-related macular degeneration; Color fundus to fundus autofluorescence generation; Deep learning; Generative adversarial networks.