fairdataihub/envision-eye-imaging-training-data huggingface dataset:
Synthetic Retinal OCT Dataset Generated using Class-Conditioned DDPM This dataset contains synthetic Optical Coherence Tomography (OCT) retinal images generated using class conditioned denoising diffusion probabilistic models (DDPMs). The images represent four diagnostic categories: choroidal neovascularization (CNV), diabetic macular edema (DME), drusen, and normal retina. Synthetic data are generated to address the limited availability of labeled OCT datasets and support the development of AI
"OD\/OC segmentation dataset" "The Retinal Fundus Glaucoma Challenge (REFUGE) dataset is our primary benchmark and is known for distribution differences between its subsets. We follow the official split with 1\\,200 training, 400 validation and 400 test images, each with expert OD\/OC masks and image-level glaucoma labels.The ORIGA dataset, collected by the Singapore Eye Research Institute, contains 650 images (168 glaucomatous) with OD\/OC annotations and glaucoma diagnosis. We use
OD localization using vessel based image orientation independent approaches Automatic Optic Disc (OD) localization is an essential problem in ophthalmic image processing. Knowing its location not only helps doctors with the early detection of sight threatening diseases but provide early diagnostic solutions for patient. With the existing methods, there are limitations on computation time and disorientated image cases. Many algorithms fail to locate the OD if the input image is tilted by a
"Ibn Al-Haitham Fundus Image Dataset" "The Ibn Al-Haitham Glaucoma Fundus Dataset is a curated medical imaging collection developed to support research in glaucoma segmentation and classification. The dataset was compiled in collaboration with the Ibn Al-Haitham Teaching Eye Hospital in Baghdad, Iraq, and includes 3,000 high-resolution retinal fundus images acquired from clinical settings over a three-month data collection period. The dataset represents a diverse patient population, including
Automatic optic disc segmentation for edema classification and severity grading Optic disc edema (ODE) is a notable ocular symptom often seen in neuro-ophthalmic illnesses affecting the optic disc (OD). The etiologies underlying ODE are diverse, including a range of symptoms and consequences. The early detection of ODE can mitigate the risk of vision loss and the development of severe ocular disorders. The texture seen in the edematous OD exhibits a notable dissimilarity when compared to the
OCTA-Mosaicking Dataset This dataset includes 19 sets of superficial capillary plexus OCTA images captured from 10 patients (right and left eyes, except for patient #6, which only includes left eye images). Each set consists of three to five overlapping images covering the area from the optic disc to the retinal temporal region, including the macular area. From these sets, wide-field OCTA mosaics can be constructed.

Each image covers an area of 6 × 6 mm with a resolution of 320 × 320 pixels
Data for "Deep learning approach for steep and flat corneal curvature prediction using fundus photography" Corneal curvature is one of the important factors consisting of refractive errors and related to several pathological conditions of the cornea. Fundus photography is the widely used tool to examine the eyes in the current healthcare setting worldwide. The aim of this study is to develop a deep learning model based on fundus photography to predict corneal curvature by categorizing the
CHASE_DB1 The CHASE_DB1 dataset is a publicly available dataset for retinal vessel segmentation. fundus photography medical image analysis medical imaging retinal blood vessels retinal vessel segmentation segmentation
Bioptigen dataset A dataset of OCT scans obtained with the Bioptigen device. Bioptigen OCT scans age-related macular degeneration diabetic retinopathy
Multi-Label Retinal Diseases (MuReD) Dataset Abstract:
Early detection of retinal diseases is one of the most important means of preventing partial or permanent blindness in patients. One of the major stumbling blocks for manual retinal examination is the lack of a sufficient number of qualified medical personnel per capita to diagnose diseases. Computer-aided diagnosis systems (CAD) have proven to be very effective in helping physicians reduce the time taken to make a diagnosis and minimize
Rotterdam EyePACS AIROGS train set - Part 1/2 The Rotterdam EyePACS AIROGS train set contains 101,442 color fundus images from 54,274 subjects and approximately 500 different sites with a heterogeneous ethnicity. All images were assigned by human experts with the labels referable glaucoma, no referable glaucoma, or ungradable. This dataset contains the data for training data of the AIROGS challenge, which is held as part of ISBI 2022. See https://airogs.grand-challenge.org/ for more information.
Outer Retinal Corrugations: Imaging Dataset and Mechanical Models Dataset of the Data In Brief publication "Outer Retinal Corrugations: Imaging Dataset and Mechanical Models", Darabad et al.
This dataset contains the raw swept-source optical coherence tomography cross-sectional scans that were used to generate the mechanical models that elucidate the formation of outer retinal corrugations in rhegmatogenous retinal detachment.
RAO (retinal artery occlusion) fundus photography dataset This dataset is a dataset collected to train a deep learning model for diagnosing retinal artery occlusion (RAO) through fundus photographs. A RAO diagnosis model should be developed to prevent a fatal disease in which blood vessels in the eye are blocked by emboli, a type of stroke. Based on web-based public data and public fundus datasets RFMiD and JSEIC. Retina Retinal Blood Vessel Fundus Imaging
External Eye Photographs for Screening Referable Blepharitis This dataset contains external eye photographs curated from publicly available web sources to support research on screening referable blepharitis using camera-only images.
Intended use: Benchmark binary classification of blepharitis vs normal on external photographs. Ocular Imaging Eyelid Disease
Biomedical image processing by using image and signal processing Biomedical image processing is to integrate the medical knowledge to the digital imaging techniques for medical diagnostics. The prior knowledge of the specific medical content of images is needed to realize. Generally, it covers four main areas: Image formation, Image Visualization, Image Analysis, and Image Management. In this research, Image enhancement and Image Analysis are mainly focused. Two case studies with different
SUSTech-SYSU dataset for Automated Exudate Detection and Diabetic Retinopathy Grading SUSTech-SYSU dataset is a collection of images of diabetic retinopathy. Diabetic Retinopathy Fundus Images Image Segmentation
FIQS Dataset (Fundus Image Quality Scores) We establish a new dataset, Fundus Image Quality Scores (FIQS), including 2246 fundus images with continuous numerical mean opinion scores (MOSs) ranging from 0 to 100 and three-level labels (``Good", ``Usable", and ``Reject"). This dataset was annotated manually by six professional ophthalmologists to represent the real critical factors affecting the fundus image quality. The images in the FIQS dataset have two resolutions: original size (preprocessed
Ichallenge-AMD The Ichallenge-AMD dataset is used for age-related macular degeneration detection, containing 1200 annotated retinal fundus images. AMD Fundus Images Retinal Disease
OCT This dataset consists of publicly available ophthalmic OCT images, including categories such as CNV (choroidal neovascularization), DRUSEN (macular drusen), and NORMAL (normal). It serves as the raw data for the study titled Super-Resolution Reconstruction of OCT Images Based on Frequency and Spatial Information in Adversarial Neural Networks. The data is sourced from a publicly available image dataset, with reference to D. Kermany, K. Zhang, and M. Goldbaum, "Labeled Optical Coherence
Optic disk detection and segmentation approaches based on vessel network Precise localization of the optic disk (OD) in retinal images is one of the challenges in ophthalmic image processing. Although many efforts have been made towards finding automated numerical solutions, they often fail on retinal images characterized by poor quality. Therefore, we introduce three novel methods namely, Vessel Transform (VT), Vessel Vector based Phase Portrait Analysis (VVPPA), and Hybrid Approach (HA) for
Diurnal Measurements of Macular Thickness and Vessel Density on OCT Angiography in Healthy Eyes and Those With Ocular Hypertension and Glaucoma The uploaded dataset refers to a prospective study made in 2018-2020
Agreement of anterior segment measurements between Scheimpflug and swept-source OCT based instruments. Descriptive values for WTW and ACD obtained with Anterion, Pentacam and IOL Master. Optometry Optical Imaging Refractive Surgery
FIVES dataset A fundus image dataset for artificial intelligence based vessel segmentation. artificial intelligence fundus image vessel segmentation
Kaggle-DR The Kaggle-DR dataset is used for diabetic retinopathy grading, containing 35,126 high-resolution fundus images. Diabetic Retinopathy Fundus Images Retinal Disease
OCTA-SVR-100K OCTA-100K-SVR is a large-scale synthetic dataset designed for training and evaluating Vision-Language Models (VLMs) on Optical Coherence Tomography Angiography (OCTA) analysis, particularly for explainable diabetic retinopathy (DR) staging. The dataset contains 100,000 pairs of synthetic OCTA images and structured reasoning texts, generated through a pathology-aware simulation pipeline.
Fundus-AVSeg AbstractRetinal artery-vein vessels are associated with systemic chronic diseases and cardiovascular diseases. Therefore, the accurate quantitative analysis of retinal artery-vein vessels is the preliminary basis of clinical diagnosis. Most of the existing artificial intelligence(AI) methods are data-driven. Although some public retinal artery-vein vessel segmentation datasets have been released, their data quality is unsatisfactory. In this paper, we establish a new fundus image
Multi-Label Retinal Disease (MuReD) Early detection of retinal diseases is one of the most important means of preventing partial or permanent blindness in patients. One of the major stumbling blocks for manual retinal examination is the lack of a sufficient number of qualified medical personnel per capita to diagnose diseases. Computer-aided diagnosis systems (CAD) have proven to be very effective in helping physicians reduce the time taken to make a diagnosis and minimize variability in image
Stanford Glaucoma Dataset A dataset of OCT scans consisting of glaucoma and non-glaucomatous cases obtained from four tertiary care eye hospitals located in four different countries. Glaucoma dataset OCT scans Optical Coherence Tomography
Finding Ophthalmic Risk and Evaluating the Value of Eye exams and their predictive Reliability (FOREVER) - dataset The Finding Ophthalmic Risk and Evaluating the Value of Eye exams and their predictive Reliability (FOREVER) dataset was developed in a collaborative effort with Synoptik A/S under the supervision of a consortium of international partners. It consists of extensive eye examinations carried out in optician stores, questionnaire responses, and for a subpopulation blood pressure
Diabetic Retinopathy Dataset The dataset used in this paper is not explicitly described. However, it is mentioned that the authors used a dataset for medical image classification, specifically for diabetic retinopathy diagnosis. Diabetic Retinopathy Medical Image Classification diabetic retinopathy doctor diagnoses examination reports fluorescein angiography images retinal fundus images
IDRiD dataset The IDRiD dataset consists of 516 fundus images and ground truths associated with the signs of Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME). diabetic macular edema diabetic retinopathy retinal fundus images
EyePACS dataset The EyePACS dataset is a collection of retinal fundus images of individuals potentially suffering from diabetic retinopathy (DR), an eye disease associated with diabetes that is one of the leading causes of visual impairment worldwide. Diabetic Retinopathy Grading Image Analysis diabetic retinopathy medical imaging retinal fundus images
MESSIDOR With Lesion Annotations This dataset is derived from the original MESSIDOR dataset, enhanced with annotations for 10 pathological lesions and laser photocoagulation scars commonly observed in diabetic retinopathy. The annotated lesions include microaneurysms, dot and blot hemorrhages, cotton wool spots, intra-retinal microvascular abnormalities, venous looping, new vessels at the disc, new vessels elsewhere, hard exudates, fibrous proliferans, and pre-retinal or vitreous hemorrhage.
Fractal evaluation in retinographies of individuals with different grades of diabetic retinopathy ABSTRACT Purpose To evaluate the retinal blood vascular network of the retinographies of patients with different grades of diabetic retinopathy. Methods Ninety Retinographies (MESSIDOR database) were used, with different grades of diabetic retinopathy divided into 4 groups: no retinopathy (n=23), grade one (n=20), grade two (n=20) and grade three (n=27) diabetic retinopathy. The grades of diabetic
Optic disc segmentation applied to retinal images with glaucoma, diabetic retinopathy, and optic disc edema Precise detection of the optic disc (OD) is an important task in the diagnosis of several eye diseases. To manage the massive visual-impaired population, there is a huge demand for efficient and remote retinal imaging techniques that can visualize the health of the retina. In this regard, the use of handheld mobile cameras attached to a smartphone is a promising approach. With the
DRIVE The DRIVE dataset contains the curvilinear-shaped vessel. This dataset consists of 40 565 × 584 color retinal images, which are split into 20 training images and 20 test images. Computer Vision DRIVE Digital Retinal Images In-Context Learning Medical Image Analysis Medical Image Segmentation Meta-driven Visual Prompt Selection Retinal Vessel Segmentation Vessel Extraction diabetes
The Foveal Avascular Zone Image Database (FAZID) The Foveal Avascular Zone (FAZ) is of clinical importance since the vascular arrangement around the fovea changes with disease and refractive state of the eye. In order to test and validate newly developed automated segmentation algorithms, we have created a public dataset of these retinal fundus images consisting of a total of 304 different images classified into: Diabetic (107), Myopic (109) and Normal (88) eyes. The images are of dimensions
Minimal dataset for the Casia 2 anterior segment OCT repeatability study This dataset contains repeat measurements in 300 eyes performed with the Tomey Casia2 anterior segment OCT. The special focus is set to the multivariate analysis of the power vectors including front and back surface measurements, RealPower, and the Fourier decoposition of the corneal front & back surface and RealPower data evaluated within the 3 and 6 mm zone Ophthalmology and optometry not elsewhere classified
HVDROPDB Datasets for Classification and Segmentation for Research in Retinopathy of Prematurity, Ranjana Agrawal HVDROPDB_RetCam_Neo_Segmentation and HVDROPDB_RetCam_Neo_Classification are the first datasets to be published for the retinal structure segmentation to identify the Retinopathy of Prematurity (ROP). They are prepared by screening the preterm infants visiting PBMA's H.V. Desai Eye Hospital, Pune with two diverse imaging systems RetCam and Neo. The Segmentation dataset contains
DRIVE Dataset The DRIVE dataset includes 40 color fundus photographs divided into two parts: 20 training images and 20 test images with manual segmentation of the vessels and binary masks of the Field of View. DRIVE Dataset Diabetic Retinopathy Retinal Image Segmentation Retinal Vessel Segmentation
Topcon dataset A dataset of OCT scans obtained with the Topcon machine. OCT scans Topcon central serous retinopathy diabetic retinopathy
Anatomical Characterization of an Optic Disc Notch Using SD-OCT in Glaucoma Purpose: To characterize anatomical dimensions of a disc notch using novel methods using spectral domain optical coherence tomography (SD-OCT). Participants: All age- and severity-matched glaucoma patients with disc notch (defined as complete loss of neural rim with no residual rim between disc and cup) seen from 2014 to 2015 who underwent enhanced depth imaging with SD-OCT (Cirrus HD-OCT version 6.5, Carl
Fairer AI in Ophthalmology via Implicit Fairness Learning for Mitigating Sexism and Ageism We collect the largest and most diverse fundus image dataset with data from over 8,405 patients representing a wide age range (0 to 90 years). The fundus dataset contains two types of advanced ultra-widefield and regular narrow-angle fundus images, with the ultra-widefield imaging dataset containing 16,530 fundus images annotated with 38 ophthalmic diseases and 67 fundus features and the narrow-angle
High-Resolution Retinal Fundus Dataset for Diabetic Retinopathy Detection from Aizawl This dataset contains high-resolution retinal fundus images collected from 495 unique subjects from Eye Care hospital in Aizawl, Mizoram, for diabetic retinopathy (DR) detection and classification. The images were captured over five years using the OCT RS 330 device, which features a 45° field of view (33° for small-pupil imaging), a focal length of 45.7 mm, and a 6.25 mm sensor width. Each image was acquired
Red lesion localization for MESSIDOR retinal images dataset using patch-based convolutional neural networks. Red lesion localization for MESSIDOR retinal images dataset using patch-based convolutional neural networks. Medical Imaging Retina Retinal Disease
SLID-E: Slit Lamp Image Dataset for Epiphora - A Benchmark Resource for Automated Tear Overflow Analysis We introduce SLID-E (Slit Lamp Imaging Dataset for Epiphora), the first comprehensive dataset for epiphora grading using slit lamp photographs. The dataset comprises 2,999 high-quality images from 1,563 patients, annotated by expert ophthalmologists into four categories: normal, mild, moderate, and severe epiphora. Ophthalmology
Retinal Cross-Domain Dataset A retinal cross-domain dataset consisting of 1,179 OCT volumes (57,771 B-scans after re-sampling Cirrus OCTs) with a total of 11,172 B-scans (66 Heidelberg Spectralis and 162 Zeiss Cirrus volumes). Medical Dataset OCT Retinal Imaging
AROMA dataset AROMA dataset (Artifact Recognition on OCT-A, Modeling, and Analysis)

General description

One of the purpose of the AROMA study was to release a publicly available database, consisting of en face retinal SS OCT-A images labeled by artifact type and severity.

This dataset includes:

OCT-A scans (n = 281), each decomposed into 14 .png En Face images (C-scans);

an Excel file that indicates the intensity of artifacts present and the Signal Strength for each OCT-A scan. The
Fundus Autofluorescence in Premature Infants Evaluation of fundus autofluorescence patters in premature infants. Ophthalmology Retina Retinal Imaging Ophthalmic Imaging Systems Autofluorescence Fundus Imaging
Glaucoma Screening dataset PAPILA dataset contains fundus images and clinicaldata from 244 patients, with images from both eyes of each patient. This dataset is specifically designed to support research on early glaucoma diagnosis by leveraging comprehensive data from both eyes. Additionally, it includes segmentation information for each patient’s optic disc and cup, alongside diagnostic outcomes based on clinical data. For our analysis, we focused on images
Pre-screening of healthy retinal using geometrical features extracted from OCT retinal images Optical Coherence Tomography (OCT) retinal imaging is essential in ophthalmology for the diagnosis of eye diseases. To date, identifying healthy OCT retinal images can be challenging due to the large volume of images but limited time for ophthalmologists. In this study, we developed a classification technique that differentiates between healthy and unhealthy OCT images of the retina. We applied the
Classification of Diabetic Retinopathy using Pre-Trained Deep Learning Models Diabetic Retinopathy dataset containing 1000 color fundus images from KAGGLE Deep Learning Diabetic Retinopathy Fundus Images
Hillel Yaffe Glaucoma Dataset (HYGD): A Gold-Standard Annotated Fundus Dataset for Glaucoma Detection Glaucomatous optic neuropathy (GON) is a leading cause of irreversible
blindness worldwide, affecting an estimated 64.3 million people globally with
projections reaching 111.8 million by 2040. Approximately 50% of cases remain
undiagnosed until advanced stages when vision loss becomes noticeable.
Traditional diagnosis requires comprehensive ophthalmic examinations by
specialists, creating
Fundus Image-Based Automatic Segmentation In this study, we formulate the task of retinal disease diagnosis as a supervised multiclass classification problem. The goal is to automatically assign each fundus image to one of four predefined diagnostic categories: HR, DR, papilledema, or normal. This classification is based on quantitative features derived from vessel segmentation maps, including both radiomic descriptors and the AVR. By framing the task in this way, the study aims to
An ultra-wide-field fundus image dataset for intelligent diagnosis of intraocular tumors This dataset consisted of 2,031 ultra-wide-field fundus images, encompassing five distinct types of intraocular tumors and normal images. It is provided as a zipped file containing six subfolders, each corresponding to a specific category: Normal, Choroidal Hemangioma (CH), Retinal Capillary Hemangioma (RCH), Choroidal Osteoma (CO), Retinoblastoma (RB), and Uveal Melanoma (UM). Each subfolder includes all
Optic Nerve Head Image Database (ODTiD) The Optic Disc tilt (ODT) or Tilted optic disc is a common finding in the general population and it is due to anomalous ocular development caused by the malclosure of the embryonic optic fissure. ODT is commonly associated with high myopia as well as other conditions (e.g., astigmatism, visual field loss, defective color vision, and retinal abnormalities). This dataset of ODT images contains both horizontal and vertical cross-sectional images obtained
Dataset for study on foveal avascular zone morphology, ganglion cell thickness, and high density perimetry in glaucoma This dataset contains data from a cross-sectional study evaluating foveal avascular zone (FAZ) morphology, macular vascular density (mVD), ganglion cell layer thickness (GCLT), and high density perimetry in glaucoma patients.

Each row corresponds to one eye, including variables such as age, gender, eye number, axial length, structural metrics (FAZ area, perimeter,
Diabetic CF-FA registration Keypoints Ground truth We present a publicly available dataset of keypoints for multimodal retinal image registration using Color Fundus (CF) and Fractional Anisotropy (FA) images. This dataset is derived from the diabetic retinal image dataset [1], consisting of 59 subjects (29 healthy controls and 30 diabetic patients). The CF images are captured in RGB, while FA images, highlighting microvascular structures, are provided in grayscale, both at a resolution of 720 ×
MCOA: A Comprehensive Multimodal Dataset for Advancing Deep Learning in Corneal Opacity Assessment The dataset includes 131 eyes from 116 patients with corneal opacity and 205 eyes from 158 patients with clear corneas, totaling 5,376 AS-OCT images and 336 corresponding anterior segment photographs. Each image was carefully annotated to include detailed corneal and corneal opacity information. Ophthalmology Optical technology
CTI Dataset Tri-Modal Imaging (CTI) dataset, an anonymized, multimodal, and annotated collection of medical images from Beijing Tongren Hospital (2013-2019) encompassing Fluorescence Angiography (FA), Indocyanine Green Angiography (ICGA), and Ocular Ultrasound (US) images. The construction of this dataset was approved by the Ethics Committee of Beijing Tongren Hospital. The CTI dataset includes images from patients diagnosed with benign hemangioma, secondary metastatic carcinoma to the eye, or