A synthetic, privacy-preserving collection of annotated eye images designed to prototype computer-vision pipelines (classification, detection, segmentation) before moving to real clinical data. Status: Mock / demo (non-clinical, non-diagnostic).

A dataset containing high-resolution images of the retina, including OCT scans and fundus images, designed for AI-based ophthalmology research.

The thickness and appearance of retinal layers are essential markers for diagnosing and studying eye diseases. Despite the increasing availability of imaging devices to scan and store large amounts of data, analyzing retinal images and generating trial endpoints has remained a manual, error-prone, and time-consuming task. In particular, the lack of large amounts of high-quality labels for different diseases hinders the development of automated algorithms. Therefore, we have compiled 5016 pixel-wise manual labels for 1672 optical coherence tomography (OCT) scans featuring two different diseases as well as healthy subjects to help democratize the process of developing novel automatic techniques. We also collected 4698 bounding box annotations for a subset of 566 scans across 9 classes of disease biomarker. Due to variations in retinal morphology, intensity range, and changes in contrast and brightness, designing segmentation and detection methods that can generalize to different disease types is challenging. While machine learning-based methods can overcome these challenges, high-quality expert annotations are necessary for training. Publicly available annotated image datasets typically contain few images and/or only cover a single type of disease, and most are only annotated by a single grader. To address this gap, we present a comprehensive multi-grader and multi-disease dataset fortraining machine learning-based algorithms. The proposed dataset covers three subsets of scans (Age-related Macular Degeneration, Diabetic Macular Edema, and healthy) and annotations for two types of tasks (semantic segmentation and object detection).

The thickness and appearance of retinal layers are essential markers for diagnosing and studying eye diseases. Despite the increasing availability of imaging devices to scan and store large amounts of data, analyzing retinal images and generating trial endpoints has remained a manual, error-prone, and time-consuming task. In particular, the lack of large amounts of high-quality labels for different diseases hinders the development of automated algorithms. Therefore, we have compiled 5016 pixel-wise manual labels for 1672 optical coherence tomography (OCT) scans featuring two different diseases as well as healthy subjects to help democratize the process of developing novel automatic techniques. We also collected 4698 bounding box annotations for a subset of 566 scans across 9 classes of disease biomarker. Due to variations in retinal morphology, intensity range, and changes in contrast and brightness, designing segmentation and detection methods that can generalize to different disease types is challenging. While machine learning-based methods can overcome these challenges, high-quality expert annotations are necessary for training. Publicly available annotated image datasets typically contain few images and/or only cover a single type of disease, and most are only annotated by a single grader. To address this gap, we present a comprehensive multi-grader and multi-disease dataset fortraining machine learning-based algorithms. The proposed dataset covers three subsets of scans (Age-related Macular Degeneration, Diabetic Macular Edema, and healthy) and annotations for two types of tasks (semantic segmentation and object detection).

The thickness and appearance of retinal layers are essential markers for diagnosing and studying eye diseases. Despite the increasing availability of imaging devices to scan and store large amounts of data, analyzing retinal images and generating trial endpoints has remained a manual, error-prone, and time-consuming task. In particular, the lack of large amounts of high-quality labels for different diseases hinders the development of automated algorithms. Therefore, we have compiled 5016 pixel-wise manual labels for 1672 optical coherence tomography (OCT) scans featuring two different diseases as well as healthy subjects to help democratize the process of developing novel automatic techniques. We also collected 4698 bounding box annotations for a subset of 566 scans across 9 classes of disease biomarker. Due to variations in retinal morphology, intensity range, and changes in contrast and brightness, designing segmentation and detection methods that can generalize to different disease types is challenging. While machine learning-based methods can overcome these challenges, high-quality expert annotations are necessary for training. Publicly available annotated image datasets typically contain few images and/or only cover a single type of disease, and most are only annotated by a single grader. To address this gap, we present a comprehensive multi-grader and multi-disease dataset fortraining machine learning-based algorithms. The proposed dataset covers three subsets of scans (Age-related Macular Degeneration, Diabetic Macular Edema, and healthy) and annotations for two types of tasks (semantic segmentation and object detection).

The thickness and appearance of retinal layers are essential markers for diagnosing and studying eye diseases. Despite the increasing availability of imaging devices to scan and store large amounts of data, analyzing retinal images and generating trial endpoints has remained a manual, error-prone, and time-consuming task. In particular, the lack of large amounts of high-quality labels for different diseases hinders the development of automated algorithms. Therefore, we have compiled 5016 pixel-wise manual labels for 1672 optical coherence tomography (OCT) scans featuring two different diseases as well as healthy subjects to help democratize the process of developing novel automatic techniques. We also collected 4698 bounding box annotations for a subset of 566 scans across 9 classes of disease biomarker. Due to variations in retinal morphology, intensity range, and changes in contrast and brightness, designing segmentation and detection methods that can generalize to different disease types is challenging. While machine learning-based methods can overcome these challenges, high-quality expert annotations are necessary for training. Publicly available annotated image datasets typically contain few images and/or only cover a single type of disease, and most are only annotated by a single grader. To address this gap, we present a comprehensive multi-grader and multi-disease dataset fortraining machine learning-based algorithms. The proposed dataset covers three subsets of scans (Age-related Macular Degeneration, Diabetic Macular Edema, and healthy) and annotations for two types of tasks (semantic segmentation and object detection).

This dataset consists of more than 16,000 retinal OCT B-scans from 441 cases (Normal: 120, Drusen: 160, CNV: 161) and is acquired at Noor Eye Hospital, Tehran, Iran. Images are labeled by a retinal specialist.

This dataset consists of more than 16,000 retinal OCT B-scans from 441 cases (Normal: 120, Drusen: 160, CNV: 161) and is acquired at Noor Eye Hospital, Tehran, Iran.

This repository contains composite retinal fundus and OCT dataset for analyzing retinal layers, retinal lesions, and to diagnose normal and abnormal retinal diseases like Macular Edema, Central Serous Retinopathy, Age-related Macular Degeneration, and Glaucoma.

This repository contains composite retinal fundus and OCT dataset for analyzing retinal layers, retinal lesions, and to diagnose normal and abnormal retinal diseases like Macular Edema, Central Serous Retinopathy, Age-related Macular Degeneration, and Glaucoma.

This repository contains composite retinal fundus and OCT dataset for analyzing retinal layers, retinal lesions, and to diagnose normal and abnormal retinal diseases like Macular Edema, Central Serous Retinopathy, Age-related Macular Degeneration, and Glaucoma.

This dataset is an upgraded version of the dataset used in the latter part of the article 'The possibility of the combination of OCT and fundus images for improving the diagnostic accuracy of deep learning for age-related macular degeneration: a preliminary experiment' in Medical & Biological Engineering & Computing volume 57, pages677-687(2019). Images including both OCT and matched fundus photograph was crawled from Google Image Search using keywords related to normal retina, drusen, AMD, and OCT. The dataset was built by harvesting images manually for normal and AMD categories. Finally, 59 normal eyes, 26 drusens, and 98 AMD eyes including both OCT and matched fundus images were obtained.

Fovea-centered OCT images of adult retina diagnosed with Age-related Macular Degeneration.