  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.
