OCTA-500

OCTA-500Optical coherence tomography angiography (OCTA) is a retinal imaging modality that allows a micron-level resolution to present the three-dimensional structure of the retinal vascular.We propose a new dataset dubbed OCTA-500, which contains OCTA imaging under two fields of view (FOVs) from 500 subjects. The dataset provides rich images and annotations including two modalities (OCT/OCTA volumes), six types of projections, four types of text labels (age / gender / eye / disease) and seven types of segmentation labels (large vessel / capillary / artery / vein / 2D FAZ / 3D FAZ / retinal layers). Get Password:To get the password of the compressed package, an application email must be sent to [e-mail] with a specified form like below, otherwise may be ignored.Title of Mail: OCTA500: your_organization: your_nameNote that: The string of 'OCTA500' can not be empty. It is the fixed form and a special sign we use to identifying your downloading intention from other disturbers like spams.Body of Mail:Organization Detail: Your Organization DetailsMain Works: Your Main WorksUsages: YourUsages About This Data Set Related Papers:Dataset:Mingchao Li, Kun Huang, Qiuzhuo Xu, Jiadong Yang, Yuhan Zhang, Zexuan Ji, Keren Xie, Songtao Yuan, Qinghuai Liu, and Qiang Chen. "OCTA-500: A Retinal Dataset for Optical Coherence Tomography Angiography Study," Medical Image Analysis, 2024: 103092.Vessel segmentation:Mingchao Li, Yerui Chen, Zexuan Ji, Keren Xie, Songtao Yuan, Qiang Chen, and Shuo Li."Image projection network: 3D to 2D image segmentation in OCTA images," IEEE Trans. Med. Imaging, vol.39, no.11, pp.3343-3354, 2020.Mingchao Li, Weiwei Zhang, and Qiang Chen. "Image magnification network for vessel segmentation in OCTA images," in Chinese Conference on Pattern Recognition and Computer Vision. arXiv:2110.13428, 2022.Mingchao Li, Kun Huang, Zetian Zhang, Xiao Ma, and Qiang Chen. "Label adversarial learning for skeleton-level to pixel-level adjustable vessel segmentation," arXiv: 2205.03646, 2022.Layer segmentation:Yuhan Zhang, Chen Huang, Mingchao Li, Sha Xie, Keren Xie, Songtao Yuan, and Qiang Chen. "Robust layer segmentation against complex retinal abnormalities for en face OCTA generation," in MICCAI, 2020.Jiadong Yang, Yuhui Tao, Qiuzhuo Xu,Yuhan Zhang, Xiao Ma, Songtao Yuan, and Qiang Chen. "Self-supervised sequence recovery for semi-supervised retinal layer segmentation," IEEE Journal of Biomedical and Health Informatics, vol.26, no.8, pp.3872-3883, 2022.FAZ segmentation:Qiuzhuo Xu, Weiwei Zhang, Hongjing Zhu, and Qiang Chen. "Foveal avascular zone volume: a new index based on optical coherence tomography angiography images," Retina, vol.41, no.3, pp.595-601, 2021.Qiuzhuo Xu, Mingchao Li, Nairong Pan, Qiang Chen, and Weiwei Zhang. "Priors-guided convolutional neural network for 3D foveal avascular zone segmentation," Optics Express, vol.30, no.9, pp.14723-14736, 2022.Dataset Update Log: [2020.10] OCTA-500 was released, including labels for Large Vessels and FAZ[2021.3] Added Capillary labels[2022.3] Added 3D FAZ labels[2022.7] Added Artery-Vein labels and Layer segmentation labels[2023.10] Optimized Capillary labels[2023.11] Optimized Layer segmentation labelsDataset Structure:OCTA-500 includes two subsets: OCTA_6M and OCTA_3M.OCTA_6M(No.10001-No.10300):FOV: 6mm*6mm*2mmVolume: 400pixel*400pixel*640pixelOCTA_3M(No.10301-No.10500):FOV: 3mm*3mm*2mmVolume: 304pixel*304pixel*640pixelBoth subsets contain the following information:OCT volumesOCTA volumesProjection MapsOCT FULL(average)OCT ILM_OPL (average)OCT OPL_BM (average)OCTA FULL (average)OCTA ILM_OPL (maximum)OCTA OPL_BM (maximum)Text LabelGenderAgeOS/ODDiseaseSegmentation LabelLarge VesselArteryVeinCapillary2D FAZ3D FAZRetinal Layer

光学相干断层扫描血管成像（OCTA）是一种视网膜成像方式，它能够以微米级的分辨率呈现视网膜血管的三维结构。本研究提出了一种新的数据集，命名为OCTA-500，该数据集包含来自500名受试者的两种视场（FOV）下的OCTA成像。该数据集提供了丰富的图像和注释，包括两种模态（OCT/OCTA体数据）、六种类型的投影、四种类型的文本标签（年龄/性别/眼睛/疾病）以及七种类型的分割标签（大血管/毛细血管/动脉/静脉/二维FAZ/三维FAZ/视网膜层）。获取密码：为获取压缩包的密码，必须发送一封符合以下格式的要求申请邮件至[e-mail]，否则可能被忽略。邮件标题：OCTA500：[您的组织名称]：[您的姓名]请注意：'OCTA500'字符串不能为空，这是固定的格式，也是我们用来识别下载意图的特殊标识，以区分其他干扰者，如垃圾邮件。邮件正文：组织详细信息：[您的组织详细信息]主要工作：[您的作品]数据集使用目的：[关于此数据集的使用目的]相关论文：数据集：[李明超等，《OCTA-500：用于光学相干断层扫描血管成像研究的视网膜数据集》，医学图像分析，2024：103092。血管分割：[李明超等，《图像投影网络：OCTA图像中的3D到2D图像分割》，IEEE医学成像杂志，第39卷，第11期，第3343-3354页，2020。]层分割：[张宇涵等，《针对复杂视网膜异常的鲁棒层分割以生成面状OCTA》，在MICCAI会议上发表，2020。FAZ分割：[徐秋桌等，《基于光学相干断层扫描血管成像图像的黄斑无血管区体积：一种新的指标》，视网膜，第41卷，第3期，第595-601页，2021。]数据集更新日志：[2020.10] OCTA-500发布，包括大血管和FAZ的标签。[2021.3] 添加毛细血管标签。[2022.3] 添加三维FAZ标签。[2022.7] 添加动脉-静脉标签和层分割标签。[2023.10] 优化毛细血管标签。[2023.11] 优化层分割标签。数据集结构：OCTA-500包括两个子集：OCTA_6M和OCTA_3M。OCTA_6M（编号10001-10300）：视场：6mm*6mm*2mm体积：400像素*400像素*640像素OCTA_3M（编号10301-10500）：视场：3mm*3mm*2mm体积：304像素*304像素*640像素两个子集均包含以下信息：OCT体数据OCTA体数据投影图OCT FULL（平均）OCT ILM_OPL（平均）OCT OPL_BM（平均）OCTA FULL（平均）OCTA ILM_OPL（最大）OCTA OPL_BM（最大）文本标签性别年龄OS/OD疾病分割标签大血管动脉静脉毛细血管二维FAZ三维FAZ视网膜层

搜集汇总

背景与挑战

背景概述

OCTA-500是一个用于光学相干断层扫描血管成像（OCTA）研究的大规模视网膜数据集，包含来自500名受试者的图像，具有两种视野（6mm和3mm）。该数据集提供多模态数据（OCT和OCTA体积）、丰富的注释（包括文本标签如年龄、性别、疾病，以及七种分割标签如血管、视网膜层和FAZ），适用于血管分割、层分割和疾病分析等医学图像分析任务。