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 1600 high-resolution 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.

该研究使用光学相干断层扫描（OCT）技术，建立了一个开放数据集（OCTDL），其中包含 1600 多幅高分辨率 OCT 图像，针对疾病组和视网膜病理进行了标记。通过应用深度学习分类技术，对该数据集进行了疾病分类的研究。