Generative Adversarial Networks Synthesize Realistic OCT Images of the Retina
This work addresses the need for realistic retinal OCT images for applications such as surgical simulation and treatment planning in ophthalmology, representing a novel domain-specific advancement.
The authors tackled the problem of synthesizing realistic Optical Coherence Tomography (OCT) images of the retina using Generative Adversarial Networks (GANs), achieving the first end-to-end application that produces images depicting recognizable pathologies like macular holes and choroidal neovascular membranes.
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.