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Related Experiment Video

Updated: Jan 17, 2026

Quantitative Fundus Autofluorescence for the Evaluation of Retinal Diseases

Quantitative Fundus Autofluorescence for the Evaluation of Retinal Diseases

Published on: March 11, 2016

AI-Based RGB Image Generation From RG Fundus Images Using Pix2Pix: Validation by Quantitative and Observer-Based

Kumiko Kato^1,2, Koki Imai³, Yoshitsugu Matsui¹

¹Department of Ophthalmology, Mie University Graduate School of Medicine, Tsu, Japan.

Translational Vision Science & Technology

|January 14, 2026

Summary

This summary is machine-generated.

Generative adversarial networks (GANs) can convert red-green (RG) fundus images to red-green-blue (RGB) images with high accuracy. This AI approach may help reinterpret old RG images for future diagnostics.

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Area of Science:

Ophthalmic imaging
Artificial intelligence in medicine
Image processing

Background:

Red-green (RG) fundus images are often captured with older systems.
Converting RG to red-green-blue (RGB) images can enhance diagnostic utility.
Generative adversarial networks (GANs) show promise in image synthesis.

Purpose of the Study:

To evaluate the accuracy and perceptual quality of RGB fundus images generated from RG images using a Pix2Pix-based GAN.
To assess the feasibility of using AI for fundus image conversion.

Main Methods:

RG images were extracted from RGB images by disabling the blue laser on an Optos system.
A Pix2Pix-based conditional GAN was trained for RG to RGB image conversion.
Quantitative metrics (SSIM, PSNR, LPIPS, MAE, RMSE) and qualitative assessment by 39 ophthalmologists were used.

Main Results:

Generated RGB images showed high structural similarity (SSIM=0.97) and perceptual quality to original RGB images.
Ophthalmologists achieved a 57.1% correct classification rate between true and AI-generated images.
Receiver operating characteristic analysis yielded an area under the curve of 0.497, indicating no significant discrimination.

Conclusions:

Pix2Pix-based GANs can generate perceptually and structurally consistent RGB images from RG images.
The AI model does not require lesion-specific attention mechanisms for effective image conversion.
This technique allows reinterpretation of legacy RG fundus images and supports future AI diagnostic applications.