fairdataihub/envision-eye-imaging-training-data huggingface dataset:
Dataset from fundus images for the study of diabetic retinopathy progression
Optical Coherence Tomography Angiography-OCTA Dataset for Diabetic Retinopathy
Indian Diabetic Retinopathy Image Dataset (IDRiD) — Segmentation and Grading Challenge
Retinal Fundus Multi-Disease Image Dataset (RFMiD) 2.0
Rotterdam EyePACS AIROGS train set - fundus images for glaucoma detection
JustRAIGS challenge training data set - Justified Referral in AI Glaucoma Screening
High-resolution structural and functional retinal imaging in mice
Multi-modal spatiotemporal phenotyping of human retinal organoid development
2023 IEEE SPS Video and Image Processing (VIP) Cup: Ophthalmic Biomarker Detection
DERMA-OCTA: OCT Angiography images for skin microvascular analysis
Abca4 inhibition in a cone-rich rodent leads to Stargardt Disease OCT and fundus images
Diabetic Glaucoma dataset combining ORIGA, REFUGE, ACRIMA fundus images
OCT-A mosaicking dataset for retinal vessel analysis
UTHealth - Fundus and Synthetic OCT-A Dataset (UT-FSOCTA)
Myopic Maculopathy Analysis Challenge 2023 - fundus image dataset
Retinal vessel segmentation challenge dataset DRIVE STARE CHASE_DB1
Glaucoma OCT dataset with RNFL thickness measurements
Age-related macular degeneration OCT B-scan image dataset
Heidelberg Spectralis OCT scans for diabetic macular edema
Zeiss Cirrus HD-OCT dataset for glaucoma progression analysis
Topcon 3D OCT fundus and cross-sectional images
Fluorescein angiography dataset for choroidal neovascularization
Fundus photography dataset for optic disc and cup segmentation
Retinal OCT images with drusen and geographic atrophy labels
OCTA dataset showing foveal avascular zone measurements
Corneal topography images for keratoconus detection
Anterior segment OCT dataset for angle closure glaucoma
Slit lamp photography dataset for cataract grading
Multi-Disease Detection in Retinal Imaging dataset
Retinal Wave Dataset - calcium imaging of developing retina
Evaluation benchmark for natural robustness of retinal vessel segmentation
Probabilistic volumetric speckle suppression in OCT using deep learning
Optical coherence tomography radiation cataract image dataset
HRF-Seg+: A Multi-Structure Annotated Fundus Image Dataset
Multimodal OCTA and Fundus Image dataset for diabetic retinopathy detection
Iraqi Retinal Fundus Diabetic Retinopathy Dataset IRFDRD
OCT Fundus Registration dataset for multimodal retinal analysis
Topological characterization of the retinal microvascular network
JRC-Multi-Modal Retinal Vessel Segmentation dataset
A Fundus Image Dataset for Domain Generalization in Joint Optic Disc and Cup Segmentation
CeraMIRScan: Mid-infrared OCT Scan Dataset for ophthalmic applications
qtOCT: quantitative transmission optical coherence tomography dataset
Mammalian animal and human retinal organ culture imaging data
Spontaneous retinal reperfusion of capillary nonperfusion OCT and fundus
Analysis on Multimodal Imaging of stealth Choroidal Neovascularization OCTA
Longitudinal changes in retinal microstructures OCT imaging data
Macular Drusen histology and OCT correlation dataset
Generalized Analysis of Vessels in Eye GAVE Challenge dataset
Automated fundus image quality assessment and segmentation dataset
Diabetic Retinopathy Detection using Retinal Images dataset
Optic disc localization using graph traversal algorithm dataset
An Image Processing Algorithm to Detect Exudates in Fundus Images
Binary operation based hard exudate detection fundus dataset
EXUDATES DETECTION FROM DIGITAL FUNDUS IMAGE dataset
COMPARATIVE STUDY OF DIABETIC RETINOPATHY K-NN dataset
AN AUTOMATIC SCREENING METHOD TO DETECT OPTIC DISC dataset
Fundus Fluorescein Angiography imaging dataset
Structural-Functional Transition in Glaucoma Assessment imaging data
Identification of ocular disease from fundus images using deep learning
To Assess Characteristics of Individuals with Disc Hemorrhage fundus imaging
Prevalence Risk Factors Clinical Correlates Age-related Macular Degeneration imaging
ResNet-n/DR Automated diagnosis of diabetic retinopathy fundus dataset
Eye fundus oxygenation mapping from color retinographs dataset
Machine learning classifiers for detection of glaucoma OCT dataset
Enhancing Retinal Disease Detection with Swin Transformer fundus dataset
Development of hybrid framework to characterize red lesions in fundus images
Diabetic retinopathy classification using deep convolutional neural networks fundus
EARLY DETECTION OF HIGH BLOOD PRESSURE AND DIABETIC RETINOPATHY fundus images
Diagnosis Of Diabetic Retinopathy: fundus image analysis dataset
FUNDUS IMAGES FOR DIAGNOSIS OF DIABETIC RETINOPATHY dataset
Retinal status analysis method based on feature extraction OCT dataset
Data from Inactivation of adenosine receptor retinal imaging
Polarisation camera dSTORM datasets of retinal cells
Scanning dynamic light scattering optical coherence tomography retinal flow
MedIMeta: multi-domain medical imaging including retinal fundus
Detection of Exudates Caused By Diabetic Retinopathy in Fundus Retinal Image Using Fuzzy K Means and Neural Network
Indian Diabetic Retinopathy Image Dataset (IDRiD)
Efficient Diabetic Retinopathy Space Subjective Fuzzy Clustering In Digital Fundus Images
REFUGE-2 The REFUGE-2 dataset is an active challenge on classification of clinical Glaucoma. Classification Glaucoma Classification Medical Images
Messidor dataset The Messidor dataset is a collection of 400 eye images with a severity score assigned by a medical expert. The dataset is used to evaluate the performance of the distorted greedy algorithm and its randomized variant. Messidor dataset eye images medical diagnosis
Glaucoma Dataset: EyePACS AIROGS - Light
Dataset-screening for primary open angle glaucoma Dataset- screening for primary open angle glaucoma
Bajwa Hospital (Multi Eye Disease Dataset) This Multi Disease Dataset was created for the study entitled "A Novel Hybrid Algorithm for Feature Selection Based On Optimization." The dataset was collected and compiled on April 20, 2022, at the "Bajwa Hospital, Dina Nagar, Gurdaspur, India". The dataset was created using image assets from three different eye modalities. It contains images of the eye categorised into four distinct categories: normal, cataract, glaucoma, and retinal disease. The
DPDR: A Dataset of Retinal Fundus images of Healthy and Proliferative Diabetic Retinopathy Patients
REFUGE18 A retinal fundus glaucoma challenge dataset. 2D images medical images semantic segmentation
Dataset of Retinal blood Vessels of Healthy and Diabetic Retinopathy Individuals This dataset contains 50 retinal blood vessels extracted from retinal images available in Diabetic Retinopathy Image Database (DRiDB) public database. The database contains 50 colour fundus images that were captured with Zeiss VISUCAM 200 fundus camera at 45o field of view. 36 of these images has signs of DR while 14 does not show any signs of DR. Clinical Diabetes Medical Image Processing
Indian Diabetic Retinopathy Image Dataset A dataset of retinal images for diabetic retinopathy grading. Diabetic Retinopathy Medical Image Classification Retinal Images
Synthetic OCT Glaucoma Dataset
STARE Automatic segmentation of retinal vessels in fundus images plays an important role in the diagnosis of some diseases such as diabetes and hypertension. STARE STARE dataset diabetes fundus images hypertension medical image analysis medical imaging retinal blood vessels retinal vessel segmentation retinal vessels
Stare dataset The Stare dataset is a collection of 373 retinal images with a severity score assigned by a medical expert. The dataset is used to evaluate the performance of the distorted greedy algorithm and its randomized variant. Stare dataset medical diagnosis retinal images
Advanced Imaging for Glaucoma Study Dataset for Advanced Imaging for Glaucoma Study Glaucoma Optical Coherence Tomography Visual Field
Optic disc localization using vessel clustering and rotational 2D vessel projection Detecting optic disc (OD) in a retinal image is important for the diagnosis of numerous eye diseases. It helps ophthalmologists identify health condition of an eye. This thesis presents two effective methods for analyzing the retinal vessel structure that would be used for locating OD.The first method is Retinal Vessel Clustering. After acquiring vessel data from retinal vessel segmentation, length and average
Fundus Images Dataset The fundus images dataset is used for medical image enhancement. It contains 5,575 consecutive patients' fundus images. Fundus Images Medical Image Enhancement Ophthalmology
Mule_Fundus_Image Fundus images are captured by handheld fundus camera. Currently, only part of fundus image are annotated with forvea location. Compared to desktop fundus camera, handheld camera suffers more unstable capturing and uncontrolled envrionments. We are now focusing on the enhancement algorithm for this kind of low-quality fundus image.
The Mule dataset is a fundus image dataset with reflection of retinal nerve fiber layer (RNFL). It is composed of 538 color fundus images captured
EyePACS DR CVS Data Preliminary dataset used to generate abstract for research paper. Courtesy of EyePACs. Diabetic Retinopathy Fundus Imaging
Fundus dataset for detection of Diabetic Retinopathy Fundus images with dimensions 3680x3288 collected at Natasha Eye Care and Research Institute in Pune, Maharashtra, India. The Symbiosis Institutional Ethics Committee (BHR) SIU granted authorization for the project under approval code SIU/IEC/583. The images were captured using a nonmydriatic as per the protocol, and Fundus images using an Eidon machine. A Fundus scan requires 1-2 minutes if the patient is cooperative. Two ophthalmologists
Retinal OCT images The dataset consists of retinal optical coherence tomography (OCT) images captured from two different devices, Cirrus and Spectralis, both of which are widely used in clinics. It includes detailed label information for images captured from Cirrus.
Blood Vessels Dataset Obtained From Retina Images Of Healthy And Diabetic Retinopathy Individual This dataset contains blood vessels image files extracted from publicly available fundus retina images Blood Vessels, Diabetic Retinopathy, DRIDB, Segmentation
Annotated dataset for retinal fundus image segmentation; Vascular arcade and optic nerve This repository contains a set of manually generated segmentation masks derived from retinal fundus images from the public APTOS 2019 Blindness Detection dataset. The annotations correspond to two relevant anatomical structures in ophthalmology: the vascular arcade and the optic nerve. The dataset includes 500 masks in PNG format, along with annotation files in COCO (JSON) format and Python scripts used to
Diabetic Retinopathy The dataset consists of color fundus images categorized into five Diabetic Retinopathy (DR) severity levels. The No_DR category, with 1,805 images, represents healthy retinal scans without any signs of DR. The Mild category includes 370 images, showing early signs of microaneurysms, the initial indicators of DR. The Moderate class, comprising 999 images, exhibits more prominent abnormalities such as hemorrhages and exudates, indicating disease progression. The Severe
Messidor-2 The dataset used for the diabetic retinopathy diagnosis and uncertainty quantification task. Deep Learning Diabetic Retinopathy Fundus Image Medical Images Medical Imaging
3D OCTA Microvascular Annotations Two OCTA datasets were employed in this study. The first is a clinical dataset that includes scans from both healthy individuals and patients with diabetic retinopathy, acquired at a typical macular field of view (3 × 3 mm). The second dataset consists of annotated scans from healthy subjects with the same imaging resolution. These datasets were used to validate the proposed method in terms of segmentation accuracy, robustness, and generalizability across
Glaucoma Dataset: EyePACS-AIROGS-light-V2
Kaggle-EyePACS A public diabetic retinopathy dataset containing 88,702 images with image-level grading labels. diabetic retinopathy grading
DRIVE and CHASE DB1 datasets Two benchmark datasets for blood vessel segmentation in retinal images blood vessel segmentation medical image analysis retinal images
Duke University dataset Segmentation of retinal OCT B-scans into 7 retinal layers and accumulated fluid. Fluid segmentation Medical Imaging OCT B-scans Retinal layer segmentation
Datasets from "In vivo volumetric analysis of retinal vascular hemodynamics in mice with spatio-temporal optical coherence tomography" Dataset DescriptionThis repository contains the raw data files that were used to generate Figures 2, 3, 4, 5, 6, and 8 in the manuscript. The file names correspond to the figure (or subfigure) numbers in which they were used, allowing for straightforward cross-referencing between the data and visualized results.Data OverviewThe study is based on the analysis
Maetschke et al. dataset The dataset used in this study for glaucoma detection from 3D OCT imaging. 3D OCT imaging Deep learning Glaucoma detection
Multimodal OCTA and Fundus Image dataset for detection of Diabetic Retinopathy This is a Multimodal OCTA and Fundus Image dataset curated for detection of Diabetic Retinopathy at Natasha Eye care and Research Institute. In all 224 images from 74 persons were captured. Two modalities for every individual were taken simultaneously using Eidon camera for Fundus images and Optovue Avanti edition for OCTA images. we have three categories of classification in this dataset 1. NO DR 2. MILD DR 3.
CORN CORN1500 is a new corneal nerve dataset which was particularly designed for tortuosity grading tasks. The newly constructed CORN1500 aims to expand the dataset pool for tortuosity grading. Similar to CORN-3, all 1500 CCM in CORN1500 were acquired using a Heidelberg Retina Tomograph equipped with a Rostock Cornea Module (HRT-III) microscope. Each image has a resolution of 384 × 384 pixels covering a field of view of 400 × 400um2. All images were manually annotated by the same
Fundus dataset of MCTN classified, dataset of Myopia, Choroidal Neovascularization, Tessellation, Normal fundus Images Choroidal Disease Testbed Fundus Imaging Myopia Norm Estimate
OIMHS dataset OIMHS dataset consists of 3859 B-scan images of 119 patients with pixel annotation of retina, macular hole, intraretinal cysts, and choroid. The quality of each image was evaluated including low signal strength, signal shield and image blur. We believe this dataset will allow interested researchers to better segment macular holes and train their models, which will benefit the further development of macular hole segmentation and clinical research. Artificial Intelligence and Image
Fundus Image Dataset for Vessel Segmentation
Edge-Aware Vessel Segmentation Based on Local-to-Global Supervision ‌DRIVE Dataset‌: The DRIVE dataset consists of 40 fundus images collected from 7 patients with retinal disease and 33 healthy individuals. Each image has a resolution of 584 × 565 pixels. A total of 20 images are used for training, while the remaining 20 images are designated for testing. Each image has two annotations provided by two different experts, and we use the first annotation as the ground
An Annotated Corneal Confocal Microscopy Dataset for Nerve Segmentation and Clinical Characterization Corneal confocal microscopy enables in vivo visualization of the subbasal nerve plexus, yet most available datasets provide only centerline annotations or are restricted in accessibility, limiting pixel level segmentation research. Existing resources vary in quality, annotation strategy and clinical coverage, which complicates objective algorithm comparison and reduces their utility for
Retinal Disease Classification Description:

Retinal Disease Classification According to the WHO’s 2019 World Report on Vision, an estimated 2.2 billion people worldwide suffer from visual impairments, with at least 1 billion cases being preventable or yet to be addressed. The global eye care landscape faces significant challenges, including disparities in the accessibility and quality of prevention, treatment, and rehabilitation services. Early detection and diagnosis of ocular pathologies are
Bangladesh Multi-Disease Retinal Dataset (BMRD) This Dataset contains 1786 original color fundus images of Diabetic Retinopathy (DR), Age related Macular Degeneration (AMD) and Glaucoma Sight threatening Eye Diseases. These eye images were collected from three renowned Eye Hospitals of Bangladesh namely Khulna Eye Hospital & Laser Center Limited, Khulna BNSB Eye Hospital, Shiromoni and Anwer Khan Modern Medical College Hospital, Dhaka, Bangladesh. The images were collected and categorized
SYN-OCT The SYN-OCT dataset consists of 200,000 synthetic cross-sectional circumpapillary OCT images, including 100,000 images generated to represent glaucoma eyes and 100,000 images generated to represent healthy eyes. These synthetic images were produced using generative models trained on real OCT scans collected from participants at the Singapore Eye Research Institute. This resource is designed to support the development and validation of deep learning models for glaucoma detection and
Retinal Fluid Segmentation Dataset A dataset of 228 OCT volumes with a total of 11,172 B-scans (66 Heidelberg Spectralis and 162 Zeiss Cirrus volumes). Medical Dataset OCT Retinal Imaging
JustRAIGS challenge training data set The image data was provided by EyePACS LLC, Santa Cruz, CA, USA. Labels were provided by the Rotterdam Ophthalmic Institute, Rotterdam Eye Hospital, Rotterdam, The Netherlands.

The JustRAIGS dataset is divided into a training subset with 101,442 gradable fundus images, spanning both referable and no referable glaucomatous cases. For more information about the dataset please see this paper.

The training set, named JustRAIGS Dataset, is publicly available
Comprehensive 3D Optical Coherence Tomography Dataset for AMD and DME: Facilitating Deep-Learning-Based 3D Segmentation 3D optical coherence tomography (OCT) dataset for 3D AMD and DME segmentation. Bioinformatic methods development
Macular OCT Images Dataset A dataset of macular OCT images of healthy controls and patients with diabetic macular edema OCT images diabetic macular edema healthy controls macular OCT images
HRF The HRF dataset contains 45 images, which is divided into 15/15/15 images for healthy patients, patients with diabetic retinopathy and glaucomatous patients. diabetic retinopathy medical image analysis medical imaging retinal vessel segmentation
PALM and Eyepacs datasets The PALM and Eyepacs datasets are used for fundus disease localization. The PALM dataset contains 1200 standardized fundus images, divided into three groups: training, validation, and testing, each group containing 400 images. The Eyepacs dataset contains 35,126 diabetic retinal fundus images, with pixel sizes ranging from 433 × 289 pixels to 5184 × 3456 pixels. fundus disease localization medical imaging
DS3 A dataset of retinal images for the classification of diabetic retinopathy and age-related macular degeneration. Age-related macular degeneration Diabetic retinopathy Retinal images
"Contrast Based Circular Approximation For Accurate And Robust Optic Disc Segmentation In Retinal Images" - Code A new method for automatic optic disc localization and segmentation is presented. The localization procedure combines vascular and brightness information to provide the best estimate of the optic disc center which is the starting point for the segmentation algorithm. A detection rate of 99.58% and 100% was achieved for the Messidor and ONHSD databases, respectively. A simple circular
Isfahan MISP dataset The Isfahan MISP dataset, consisting of retinography and angiography images of diabetic patients. Diabetic patients MISP dataset Retinal images medical imaging multimodal transfer learning retinal vascular segmentation
Tunisian OCT Dataset for Multi-Disease Retinal Classification The Retinal-OCT-Clinical-Dataset is a collection of clinically acquired retinal OCT (Optical Coherence Tomography) images, designed to serve as a benchmark for deep learning models in retinal disease classification.The dataset includes images from multiple retinal pathologies: AMD (Age-related Macular Degeneration) DME (Diabetic Macular Edema) RRD (Rhegmatogenous Retinal Detachment) Healthy/Normal cases⚠️ Access:
Diabetic Retinopathy Detection The dataset used in the diabetic retinopathy detection task in the conformal risk control framework. Diabetic Retinopathy Medical Imaging diabetic retinopathy medical imaging retinal fundus photos
Fluorescein Angiography dataset The dataset is used for training and testing the proposed VTGAN model for fundus-to-angiogram synthesis and disease prediction. Fluorescein angiography Fundus photography Image synthesis Medical imaging
Optic disk localization and segmentation in retinal images using vessel based feature projection approaches and active contour model Detection of an optic disk (OD) is crucial in the analysis of abnormality conditions such as optic disk shrinking/swelling, pale optic disk, having exudates and hemorrhages in the retinal images. In addition, it is very useful for ophthalmologists in finding the diagnosis of glaucoma and other neuropathies. This thesis presents two efficient methods for OD
Cirrus dataset A dataset of OCT scans imaged with the Cirrus machine. Cirrus OCT scans central serous retinopathy diabetic retinopathy
Refined IDRiD diabetic retinopathy; dataset; image segmentation; deep learning; proliferative diabetic retinopathy; anatomical context; medical imaging
Deep Learning-Based Style Transfer from Ultra-Widefield to Traditional Fundus Photography This study included 451 anonymized UWF and 745 FP images. The ultra-widefield (UWF) images, which include both normal and pathologic retinal images, were based on Tsukazaki Optos Public Project. The traditional fundus photograph (FP) images were extracted from the publicly accessible database by using the Google image and Google dataset search that included English keywords related to retina. The search
Spectralis dataset A dataset of OCT scans obtained with the Spectralis machine. OCT scans Spectralis age-related macular degeneration diabetic retinopathy
Longitudinal Healthy Multi-modal AO Datasets With Two Timepoints Validation dataset for the paper:Chen, M., Cooper, R.F., Gee, J.C., Brainard, D.H. and Morgan, J.I., 2019. Automatic longitudinal montaging of adaptive optics retinal images using constellation matching. Biomedical Optics Express, 10(12), pp.6476-6496.
4 complete longitudinal AO imaging datasets of healthy subjects, with two timepoints. Each retinal location contains 3 channels (confocal, split detection, dark field). The
MCOA: A Comprehensive Multimodal Dataset for Advancing Deep Learning in Corneal Opacity Assessment The dataset included a total of 6,272 AS-OCT images and 392 corresponding anterior segment photographs. Each image of patients with corneal opacity was carefully annotated to include detailed cornea and corneal opacity information. Ophthalmology Optical Technology
vessel segmentation The DRIVE dataset, developed by Staal et al. (2004), utilizes the elongated structure of vessel ridges for automatic vessel classification in the Utrecht database. It consists of 40 images (565 × 584 pixels) in JPEG format, captured at a 45° field of view, divided into 20 training and 20 testing images. The CHASEDB1 dataset employs multiscale Gabor filtering and morphological transformations, comprising 28 images related to cardiovascular health from 14 students of
EyeQ Retinal fundus images have been extensively used for clinical analysis of various ocular diseases, but the quality of fundus images is critical to the diagnosis and screening of eye diseases. Diabetic Retinopathy Fundus Images Medical Images Medical Imaging Retinal Diseases
LLRetina: low-light color fundus image dataset The LLRetina dataset comprises paired low-light (LL) and high-light (HL) color fundus images. Based on flash irradiance during imaging, the dataset is divided into two subsets: LLRetina_LL1 and LLRetina_LL2. LLRetina_LL1 includes both healthy and diseased eyes, while LLRetina_LL2 includes only healthy eyes.

For LLRetina_LL1, we adopted a feature point-based image registration algorithm to estimate transformation matrices and spatially align each