# OCTID: Optical Coherence Tomography Image Database

arXiv: https://arxiv.org/abs/1812.07056 · HTML: https://mlsanity.com/paper/1812.07056/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2018.

## Built on by (most cited first, 30 of 36)

- [OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods](https://mlsanity.com/paper/2312.08255/) (2023)
- [EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model](https://mlsanity.com/paper/2504.13650/) (2025)
- [Visual Anomaly Detection Via Partition Memory Bank Module and Error Estimation](https://mlsanity.com/paper/2209.12441/) (2022)
- [EyeFound: A Multimodal Generalist Foundation Model for Ophthalmic Imaging](https://mlsanity.com/paper/2405.11338/) (2024)
- [EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysis](https://mlsanity.com/paper/2409.06644/) (2024)
- [Dataset and Evaluation algorithm design for GOALS Challenge](https://mlsanity.com/paper/2207.14447/) (2022)
- [UrFound: Towards Universal Retinal Foundation Models via Knowledge-Guided Masked Modeling](https://mlsanity.com/paper/2408.05618/) (2024)
- [MultiEYE: Dataset and Benchmark for OCT-Enhanced Retinal Disease Recognition from Fundus Images](https://mlsanity.com/paper/2412.09402/) (2024)
- [Uncertainty aware and explainable diagnosis of retinal disease](https://mlsanity.com/paper/2101.12041/) (2021)
- [MIRAGE: Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis](https://mlsanity.com/paper/2506.08900/) (2025)
- [Anomaly Detection in Retinal Images using Multi-Scale Deep Feature Sparse Coding](https://mlsanity.com/paper/2201.11506/) (2022)
- [Circumpapillary OCT-Focused Hybrid Learning for Glaucoma Grading Using Tailored Prototypical Neural Networks](https://mlsanity.com/paper/2106.13551/) (2021)
- [MM-Retinal V2: Transfer an Elite Knowledge Spark into Fundus Vision-Language Pretraining](https://mlsanity.com/paper/2501.15798/) (2025)
- [FunBench: Benchmarking Fundus Reading Skills of MLLMs](https://mlsanity.com/paper/2503.00901/) (2025)
- [Continual Retinal Vision-Language Pre-training upon Incremental Imaging Modalities](https://mlsanity.com/paper/2506.19320/) (2025)
- [A Survey of Multimodal Ophthalmic Diagnostics: From Task-Specific Approaches to Foundational Models](https://mlsanity.com/paper/2508.03734/) (2025)
- [WaveNet-SF: A Hybrid Network for Retinal Disease Detection Based on Wavelet Transform in Spatial-Frequency Domain](https://mlsanity.com/paper/2501.11854/) (2025)
- [Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey](https://mlsanity.com/paper/2107.00115/) (2021)
- [Automated segmentation and extraction of posterior eye segment using OCT scans](https://mlsanity.com/paper/2109.10000/) (2021)
- [Application of Homomorphic Encryption in Medical Imaging](https://mlsanity.com/paper/2110.07768/) (2021)
- [Masked Image Modelling for retinal OCT understanding](https://mlsanity.com/paper/2405.14788/) (2024)
- [Open-Source Periorbital Segmentation Dataset for Ophthalmic Applications](https://mlsanity.com/paper/2409.20407/) (2024)
- [Multi-Modal Explainable Medical AI Assistant for Trustworthy Human-AI Collaboration](https://mlsanity.com/paper/2505.06898/) (2025)
- [ROCT-Net: A new ensemble deep convolutional model with improved spatial resolution learning for detecting common diseases from retinal OCT images](https://mlsanity.com/paper/2203.01883/) (2022)
- [Benchmarking of Query Strategies: Towards Future Deep Active Learning](https://mlsanity.com/paper/2312.05751/) (2023)
- [Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends](https://mlsanity.com/paper/2501.04073/) (2025)
- [Clinician-Friendly Foundation Models for Ophthalmic Image Diagnostics without Fine-Tuning or Technical Barriers](https://mlsanity.com/paper/2504.15928/) (2025)
- [BenchReAD: A systematic benchmark for retinal anomaly detection](https://mlsanity.com/paper/2507.10492/) (2025)
- [Randomized-MLP Regularization Improves Domain Adaptation and Interpretability in DINOv2](https://mlsanity.com/paper/2511.05509/) (2025)
- [Enhancing Diagnostic Reliability of Foundation Model with Uncertainty Estimation in OCT Images](https://mlsanity.com/paper/2406.16942/) (2024)

## Abstract (arXiv)

Optical coherence tomography (OCT) is a non-invasive imaging modality which is widely used in clinical ophthalmology. OCT images are capable of visualizing deep retinal layers which is crucial for early diagnosis of retinal diseases. In this paper, we describe a comprehensive open-access database containing more than 500 highresolution images categorized into different pathological conditions. The image classes include Normal (NO), Macular Hole (MH), Age-related Macular Degeneration (AMD), Central Serous Retinopathy (CSR), and Diabetic Retinopathy (DR). The images were obtained from a raster scan protocol with a 2mm scan length and 512x1024 pixel resolution. We have also included 25 normal OCT images with their corresponding ground truth delineations which can be used for an accurate evaluation of OCT image segmentation. In addition, we have provided a user-friendly GUI which can be used by clinicians for manual (and semi-automated) segmentation.