眼部oct图像疾病分类数据集

本数据集供课题三元学习自推断医学影像诊断系统的眼科影像分类部分使用。
数据集内容:752例非RP患者的1720张OCT图像和38例RP患者的122张OCT图像。
数据来源:由汕头眼科国际中心提供，使用设备为海德堡和拓普康。
采集地点与时间:2022年汕头眼科国际中心。
采集方案：采用标准化采集流程，由专业操作人员在汕头眼科国际中心完成。每位患者在固定海德堡和拓普康OCT设备下进行影像扫描，确保影像数据的高一致性。根据患者眼部条件调整OCT设备参数，包括扫描范围和分辨率设置，以获得高质量影像。通过多次扫描获取完整的视网膜断层结构，确保关键病变区域被准确记录。每位患者均采集多次，确保扫描结果的清晰度和稳定性，对采集到的OCT图像进行分类和存储，采集影像后将其转存为.jpg格式文件。
采集时间：2019.6-2022.6
设备状况：海德堡和拓普康OCT设备

This dataset is utilized for the ophthalmic image classification module of the triplet learning-based self-inferring medical image diagnosis system in this research project.
Dataset Content: 1720 OCT images from 752 non-RP patients, and 122 OCT images from 38 RP patients.
Data Source: Provided by Shantou International Eye Center, using Heidelberg and Topcon OCT devices.
Collection Location and Time: Shantou International Eye Center, 2022.
Collection Protocol: Standardized collection procedures were adopted and completed by professional operators at Shantou International Eye Center. Each patient underwent image scanning using fixed Heidelberg and Topcon OCT devices to ensure high consistency of the image data. Parameters of the OCT devices, including scanning range and resolution settings, were adjusted according to the patient's ocular conditions to obtain high-quality images. Complete retinal tomographic structures were acquired via multiple scans to ensure accurate recording of key lesion regions. Multiple scans were performed for each patient to guarantee the clarity and stability of the scanning results. The collected OCT images were classified and stored, and converted to .jpg format files after acquisition.
Collection Period: June 2019 to June 2022.
Device Specifications: Heidelberg and Topcon OCT devices.

搜集汇总

背景与挑战

背景概述

该数据集是一个用于眼科疾病分类的OCT图像集合，包含1842张图像，覆盖非RP和RP患者，数据来源于汕头眼科国际中心，采集时间跨度为2019年至2022年，使用专业设备确保高质量。数据集适用于医学影像AI研究，特别是元学习系统开发，数据量13.77GB，格式为压缩文件和说明文档。