A Composite Retinal Fundus and OCT Dataset with Detailed Clinical Markings of Retinal Layers and Retinal Lesions to Grade Macular and Glaucomatous Disorders

This repository contains composite retinal fundus and OCT dataset for analyzing retinal layers, retinal lesions, and to diagnose normal and abnormal retinal diseases like Centrally Involved Diabetic Macular Edema, Acute Central Serous Retinopathy (CSR), Chronic CSR, Geographic Age-related Macular Degeneration (AMD), Neovascular AMD, and Glaucoma. Please cite the following papers if you want to use any part of this dataset: 1) T. Hassan, M. U. Akram, M. F. Masood, U. Yasin, “Deep structure tensor graph search framework for automated extraction and characterization of retinal layers and fluid pathology in retinal SD-OCT scans”, Computers in Biology and Medicine, December 2018. 2) T. Hassan, M. U. Akram, N. Werghi, and N. Nazir, “RAG-FW: A hybrid convolutional framework for the automated extraction of retinal lesions and lesion-influenced grading of human retinal pathology,” IEEE Journal of Biomedical and Health Informatics, March 2020.

本数据集仓库收录了复合式视网膜眼底与光学相干断层扫描（Optical Coherence Tomography, OCT）数据集，可用于视网膜层结构、视网膜病变的分析，以及正常与异常视网膜疾病的诊断，覆盖的疾病类型包括中心受累性糖尿病性黄斑水肿、急性中心性浆液性视网膜病变（CSR）、慢性中心性浆液性视网膜病变、地理性年龄相关性黄斑变性（AMD）、新生血管性AMD以及青光眼。若您使用本数据集的任意部分，请引用以下论文：1) T. Hassan、M. U. Akram、M. F. Masood、U. Yasin，《用于自动化提取与表征视网膜光谱域光学相干断层扫描（Spectral Domain Optical Coherence Tomography, SD-OCT）中视网膜层与液性病变的深度结构张量图搜索框架》（Deep structure tensor graph search framework for automated extraction and characterization of retinal layers and fluid pathology in retinal SD-OCT scans），《Computers in Biology and Medicine》，2018年12月。2) T. Hassan、M. U. Akram、N. Werghi、N. Nazir，《RAG-FW：用于自动化提取视网膜病变并开展人类视网膜病变的病变影响分级的混合卷积框架》（RAG-FW: A hybrid convolutional framework for the automated extraction of retinal lesions and lesion-influenced grading of human retinal pathology），《IEEE Journal of Biomedical and Health Informatics》，2020年3月。

搜集汇总

背景与挑战

背景概述

该数据集是一个复合视网膜数据集，结合了眼底图像和光学相干断层扫描（OCT）数据，用于分析视网膜层和病变，以诊断黄斑和青光眼相关疾病，如糖尿病性黄斑水肿和年龄相关性黄斑变性。数据集中包含详细的临床标记，支持自动化提取和病理分级研究，并附有相关学术论文引用，适用于生物医学和计算机辅助诊断领域。