  Optical coherence tomography (OCT) is a non-invasive imaging technique with
extensive clinical applications in ophthalmology. OCT enables the visualization
of the retinal layers, playing a vital role in the early detection and
monitoring of retinal diseases. OCT uses the principle of light wave
interference to create detailed images of the retinal microstructures, making
it a valuable tool for diagnosing ocular conditions. This work presents an
open-access OCT dataset (OCTDL) comprising over 2000 OCT images labeled
according to disease group and retinal pathology. The dataset consists of OCT
records of patients with Age-related Macular Degeneration (AMD), Diabetic
Macular Edema (DME), Epiretinal Membrane (ERM), Retinal Artery Occlusion (RAO),
Retinal Vein Occlusion (RVO), and Vitreomacular Interface Disease (VID). The
images were acquired with an Optovue Avanti RTVue XR using raster scanning
protocols with dynamic scan length and image resolution. Each retinal b-scan
was acquired by centering on the fovea and interpreted and cataloged by an
experienced retinal specialist. In this work, we applied Deep Learning
classification techniques to this new open-access dataset.
