# Generative Adversarial Networks Synthesize Realistic OCT Images of the Retina

arXiv: https://arxiv.org/abs/1902.06676 · HTML: https://mlsanity.com/paper/1902.06676/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2019.

## Built on by (most cited first, 1 of 1)

- [Creating Realistic Anterior Segment Optical Coherence Tomography Images using Generative Adversarial Networks](https://mlsanity.com/paper/2306.14058/) (2023)

## Abstract (arXiv)

We report, to our knowledge, the first end-to-end application of Generative Adversarial Networks (GANs) towards the synthesis of Optical Coherence Tomography (OCT) images of the retina. Generative models have gained recent attention for the increasingly realistic images they can synthesize, given a sampling of a data type. In this paper, we apply GANs to a sampling distribution of OCTs of the retina. We observe the synthesis of realistic OCT images depicting recognizable pathology such as macular holes, choroidal neovascular membranes, myopic degeneration, cystoid macular edema, and central serous retinopathy amongst others. This represents the first such report of its kind. Potential applications of this new technology include for surgical simulation, for treatment planning, for disease prognostication, and for accelerating the development of new drugs and surgical procedures to treat retinal disease.