# OCTA-500: A Retinal Dataset for Optical Coherence Tomography Angiography Study

arXiv: https://arxiv.org/abs/2012.07261 · HTML: https://mlsanity.com/paper/2012.07261/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2020.

## Built on by (most cited first, 30 of 52)

- [Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts](https://mlsanity.com/paper/2312.02567/) (2023)
- [Topologically Faithful Multi-class Segmentation in Medical Images](https://mlsanity.com/paper/2403.11001/) (2024)
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- [Self-supervised learning via inter-modal reconstruction and feature projection networks for label-efficient 3D-to-2D segmentation](https://mlsanity.com/paper/2307.03008/) (2023)
- [Novel OCT mosaicking pipeline with Feature- and Pixel-based registration](https://mlsanity.com/paper/2311.13052/) (2023)
- [Memory-efficient High-resolution OCT Volume Synthesis with Cascaded Amortized Latent Diffusion Models](https://mlsanity.com/paper/2405.16516/) (2024)
- [Simulation-Based Segmentation of Blood Vessels in Cerebral 3D OCTA Images](https://mlsanity.com/paper/2403.07116/) (2024)
- [Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations](https://mlsanity.com/paper/2502.16697/) (2025)
- [Deep Multimodal Fusion of Data with Heterogeneous Dimensionality via Projective Networks](https://mlsanity.com/paper/2402.01311/) (2024)
- [Retinal IPA: Iterative KeyPoints Alignment for Multimodal Retinal Imaging](https://mlsanity.com/paper/2407.18362/) (2024)
- [GlaLSTM: A Concurrent LSTM Stream Framework for Glaucoma Detection via Biomarker Mining](https://mlsanity.com/paper/2408.15555/) (2024)
- [OCTAMamba: A State-Space Model Approach for Precision OCTA Vasculature Segmentation](https://mlsanity.com/paper/2409.08000/) (2024)
- [SAM-OCTA2: Layer Sequence OCTA Segmentation with Fine-tuned Segment Anything Model 2](https://mlsanity.com/paper/2409.09286/) (2024)
- [Fine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis](https://mlsanity.com/paper/2503.09808/) (2025)
- [OCT-SelfNet: A Self-Supervised Framework with Multi-Modal Datasets for Generalized and Robust Retinal Disease Detection](https://mlsanity.com/paper/2401.12344/) (2024)
- [X-GAN: A Generative AI-Powered Unsupervised Model for Main Vessel Segmentation of Glaucoma Screening](https://mlsanity.com/paper/2503.06743/) (2025)
- [UCS: A Universal Model for Curvilinear Structure Segmentation](https://mlsanity.com/paper/2504.04034/) (2025)
- [XOCT: Enhancing OCT to OCTA Translation via Cross-Dimensional Supervised Multi-Scale Feature Learning](https://mlsanity.com/paper/2509.07455/) (2025)
- [Multi-OCT-SelfNet: Integrating Self-Supervised Learning with Multi-Source Data Fusion for Enhanced Multi-Class Retinal Disease Classification](https://mlsanity.com/paper/2409.11375/) (2024)
- [KLDD: Kalman Filter based Linear Deformable Diffusion Model in Retinal Image Segmentation](https://mlsanity.com/paper/2410.02808/) (2024)
- [GlaBoost: A Multimodal Structured Framework for Glaucoma Risk Stratification](https://mlsanity.com/paper/2508.03750/) (2025)
- [TopoSculpt: Betti-Steered Topological Sculpting of 3D Fine-grained Tubular Shapes](https://mlsanity.com/paper/2509.03938/) (2025)
- [BioVessel-Net and RetinaMix: Unsupervised Retinal Vessel Segmentation from OCTA Images](https://mlsanity.com/paper/2509.23617/) (2025)
- [Curvilinear Structure-preserving Unpaired Cross-domain Medical Image Translation](https://mlsanity.com/paper/2510.19679/) (2025)
- [A General Model for Retinal Segmentation and Quantification](https://mlsanity.com/paper/2602.07012/) (2026)
- [Evaluation of Blood Vessel Segmentation Methods on Hard-to-Detect Vascular Structures](https://mlsanity.com/paper/2406.13128/) (2024)
- [Enhancing Retinal Disease Classification from OCTA Images via Active Learning Techniques](https://mlsanity.com/paper/2407.15293/) (2024)
- [BreakNet: Discontinuity-Resilient Multi-Scale Transformer Segmentation of Retinal Layers](https://mlsanity.com/paper/2408.14606/) (2024)
- [KaLDeX: Kalman Filter based Linear Deformable Cross Attention for Retina Vessel Segmentation](https://mlsanity.com/paper/2410.21160/) (2024)

## Abstract (arXiv)

Optical coherence tomography angiography (OCTA) is a novel imaging modality that has been widely utilized in ophthalmology and neuroscience studies to observe retinal vessels and microvascular systems. However, publicly available OCTA datasets remain scarce. In this paper, we introduce the largest and most comprehensive OCTA 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). Then, we propose a multi-object segmentation task called CAVF, which integrates capillary segmentation, artery segmentation, vein segmentation, and FAZ segmentation under a unified framework. In addition, we optimize the 3D-to-2D image projection network (IPN) to IPN-V2 to serve as one of the segmentation baselines. Experimental results demonstrate that IPN-V2 achieves an ~10% mIoU improvement over IPN on CAVF task. Finally, we further study the impact of several dataset characteristics: the training set size, the model input (OCT/OCTA, 3D volume/2D projection), the baseline networks, and the diseases. The dataset and code are publicly available at: https://ieee-dataport.org/open-access/octa-500.