fairdataihub/envision-eye-imaging-training-data huggingface dataset:
The Leuven-Haifa High-Resolution Fundus Image Dataset for Retinal Blood Vessel Segmentation and Glaucoma Diagnosis The Leuven-Haifa dataset contains 240 disc-centered fundus images of 224 unique patients (75 patients with normal tension glaucoma, 63 patients with high tension glaucoma, 30 patients with other eye diseases and 56 healthy controls) from the University Hospitals of Leuven. The arterioles and venules of these images were both annotated by master students in medicine and corrected by
A Deep Learning Approach for Automated Detection of Shallow Anterior Chamber Depth Based on Hidden Features of Fundus Photographs The datasets are not redistributable to researchers other than those engaged in the Institutional Review Board-approved research collaborations with the B&VIIt Eye Center, South Korea. The datasets utilized during this study are not publicly available due to reasonable privacy and security concerns. Instead, a sample anonymized fundus photography data with shallow
CIL:40254, Mus musculus, CNS neuron (sensu Vertebrata). In Cell Image Library Single slice through a serial section reconstruction of the optic nerve head in the area of transition between myelinated and unmyelinated axons the mouse obtained through serial section scanning electron microscopy. This image has been downsampled from the raw data image which can be accessed using the link provided to the Cell Centered Database. For more information, see: Nguyen et al. Myelination transition zone
COph100:A Comprehensive Fundus Image Registration Dataset from Infants constituting the "RIDIRP" database Retinal image registration is vital for diagnostic therapeutic applications within the field of ophthalmology. Existing public datasets, focusing on adult retinal pathologies with high-quality images, have limited number of image pairs and neglect clinical challenges. To address this gap, we introduce COph100, a novel and challenging dataset known as the Comprehensive Ophthalmology Retinal
RetinaMNIST RetinaMNIST is a medical imaging dataset containing 5,000 images of retinal scans, each with a size of 224x224x3. Medical Images Medical Imaging Retinal Images Retinal Scan
Conjunctival and Retinal Images of Healthy Subjects and Subjects with Diabetes This dataset includes conjunctival and retinal images collected from both diabetic and healthy individuals to support research on diabetes-related vascular changes. For each subject, eight conjunctival images (four per eye: looking left, right, up, and down) are provided. Diabetic subjects additionally have corresponding left and right retinal fundus images. Metadata for diabetic participants includes classification
Repeatability and reproducibility of automatic segmentation of retinal layers in healthy subjects using Spectralis optical coherence tomography ABSTRACT Purpose: To evaluate the repeatability and reproducibility of automatic segmentation in healthy subjects using a Spectralis optical coherence tomography (OCT) system. Methods: A total of 60 eyes from 60 patients were included in this prospective study. Spectral-domain optical coherence tomography images were generated using the Spectralis OCT
Fundus Image Myopia Development (FIMD) dataset Fundus Image Myopia Development (FIMD) dataset contains 70 retinal image pairs, in which, there is obvious myopia development between each pair of images. In addition, each pair of retinal images has a large overlap area, and there is no other retinopathy. In order to perform a reliable quantitative evaluation of registration results, we follow the annotation method of Fundus Image Registration (FIRE) dataset [1] to label control points between the
JRC-Multi-Modal Retinal Vessel Segmentation Existing retinal vessel segmentation datasets are mostly fundus images. We release this multi-modal retinal vessel segmentation dataset with our paper "Universal Vessel Segmentation for Multi-Modality Retinal Images" (IEEE Trans. Image Processing, vol.34, pp.7903-7918, 2025) to facilitate the study of vessel segmentation in multi-modal retinal imaging.

The dataset has three subdatasets: JRCFA, JRCFAF and JRCIR, for the Fluorescence Angiography (FA),
Diabetic Macular Edema VQA Dataset Medical VQA dataset built from the IDRiD and eOphta datasets. The dataset contains both healthy and unhealthy fundus images. For each image, a set of pre-defined questions is generated, including questions about regions (e.g. are there hard exudates in this region?), for which an associated mask denotes the location of the region. The motivation for this dataset includes the lack of public medical VQA datasets with related questions. In our dataset, questions
Rotterdam Artery-vein Segmentation Dataset Dataset of color fundus images annotated at the Department of Ophthalmology, Erasmus MC, Rotterdam with semi-manual artery-vein segmentations. Segmentations were acquired by manual refinement of outputs from a vessel segmentation model trained on publicly-available datasets followed by coloring of the artery and vein trees in an overlapping manner. Annotations were checked for correct connectivity of artery and vein trees. Medicine, Health and Life
"A High-quality Large-scale Color Fundus Photography Images Dataset with Eleven Types of Annotations" "More and more human beings are suffering from ophthalmic diseases, and automatic analysis of images from Color Fundus Photography (CFP) using deep learning algorithms can improve the accuracy and prevalence of early screening for ophthalmic diseases. However, the performance of deep learning models is highly dependent on the training data, and the currently available retinal image datasets
Machine learning for retinal health classification of optical coherence tomography images Optical Coherence Tomography (OCT) is an essential imaging technique for diagnosing retinal diseases. While deep learning models offer high accuracy for automated OCT classification, their “black-box” nature limits clinical trust and adoption. Conversely, traditional machine learning methods are more interpretable but often depend on features that can be difficult to extract or are sensitive to image
Rotterdam EyePACS AIROGS train set - Part 2/2 This version of the dataset is deprecated. Please use https://zenodo.org/record/5793241. 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
"GLEAM: Glaucoma Lesion Evaluation and Analysis with Multi-modal Imaging" "GLEAM contains 1,200 samples with SLO fundus images, circumpapillary OCT images, and VF PD maps , covering a four-class staging (normal, early, intermediate, and advanced glaucoma)."
Fundus vascular image data CHASE DB1: Child Heart and Health Study in England(CHASE DB1) is an openly accessible dataset designed for the analysis of fundus images. The resolution of each image is 990 × 960 pixels, and the corresponding vessel segmentation has been meticulously labeled by expert ophthalmologists. CHASE DB1 serves as a valuable resource for developing and evaluating retinal vessel segmentation algorithms, facilitating enhanced comprehension and analysis of vascular structures
Data on Fundus Images for Vessels Segmentation, Detection of Hypertensive Retinopathy, Diabetic Retinopathy and Papilledema We presents a dataset of 100 fundus digital images of retina. The retinal images are taken from Armed Forces Institute of Ophthalmology (AFIO), Rawalpindi, Pakistan and annotated with the help of four expert ophthalmologists for the purpose of computer aided diagnosis of hypertensive retinopaty, diabetic retinopathy and papilledema. This dataset contains retinal blood
Pixel-Level Segmented Longitudinal Optical Coherence Tomography Data of Macular Hole Surgery Outcomes This dataset contains a comprehensive collection of longitudinal Spectral-Domain Optical Coherence Tomography (SD-OCT) B-scans from patients who underwent surgery for idiopathic full-thickness macular hole (iFTMH). Unlike static datasets, this resource tracks retinal morphological changes from the preoperative baseline through six postoperative follow-up stages (up to 48 months), enabling
Sensitivity and specificity of automated analysis of single-field non-mydriatic fundus photographs by Bosch DR Algorithm—comparison with mydriatic fundus photography (ETDRS) for screening in undiagnos Diabetic retinopathy by Bosch Algorithm data on 560 subjects Medicine, Health and Life Sciences Diabetic retinopathy
mBRSET, a Mobile Brazilian Retinal Dataset The Mobile Brazilian Retinal Dataset (mBRSET) presents a pioneering collection
of retinal images captured via portable cameras, encompassing a diverse range
of ethnic backgrounds from Itabuna, Bahia, Brazil. Comprising 5164 images from
1291 patients diagnosed with diabetes, this dataset is augmented with clinical
and demographic metadata. Its significance lies in its provision as a resource
for the development and validation of algorithms tailored for
The SUSTech-SYSU dataset for automated exudate detection and diabetic retinopathy grading Automated detection of exudates from fundus images plays an important role in diabetic retinopathy (DR) screening and evaluation, for which supervised or semi-supervised learning methods are typically preferred. However, a potential limitation of supervised and semi-supervised learning based detection algorithms is that they depend substantially on the sample size of training data and the quality of
Micro-CT of the human eye (DICOM) Micro-CT of the human eye. This dataset includes:
1) part of the globe;2) anterior chamber angle (iris and ciliary body);3) aqueous humour outflow system;4) cornea;5) lens;6) retina;7) epiretinal membrane.
X-ray study were conducted on equipment of Research and Education Center "Materials" Don State Technical University (http://nano.donstu.ru) using an Xradia Versa 520 unit (Carl Zeiss X-ray Microscopy, Inc., USA). Data exported to DICOM file
Evaluation benchmark for natural robustness evaluation of retinal vessel segmentation models A dataset contains benchmark images for natural robustness evaluation of deep learning models for retinal vessel segmentation. The dataset consists of three mainstream retinal vessel segmentation datasets: DRIVE, STARE, and CHASE_DB1.

For each dataset are provided:

images - directory containing fundus images augmented using AugOOD tool for fast image augmentation for OOD robustness
Repeatability of Macular and Optic Nerve Head Measurements by Optical Coherence Tomography Angiography in Healthy Children Aims: To evaluate the repeatability of the peripapillary and macular vascular parameters using optical coherence tomography angiography (OCT-A) in healthy children. Materials and methods: Cross-sectional study including 34 eyes of 34 healthy children. After a complete medical examination, two consecutive OCT-A exams were done using AngioPlex Cirrus 5000 (Carl
Advancing Bag-of-Visual-Words Representations for Lesion Classification in Retinal Images Diabetic Retinopathy (DR) is a complication of diabetes that can lead to blindness if not discovered in time. Automated screening algorithms have the potential to improve identification of patients who need further medical attention. However, the algorithms must be accurate in order to be useful for clinical application. The bag-of-visual-words (BoVW) algorithm utilizes a maximum-margin binary classifier
Tunisian OCT Dataset for Multi-Disease Retinal ClassificationTunisian 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
OCTA Macula Coronal Views Macula Coronal Views (MCV) dataset

This dataset is used for experiements aiming to transform opical coherence tomography angiography (OCTA) B-Scan into
innovative OCTA graphs. The results of the research will be submitted in a paper titled as:
"New macular vasculature graphs in optical coherence tomography angiography".

The dataset contains 129 subjects that were organized as follows:

90 Subjects of Normal OCTA scans
29 Subjects of Diabetic Retinopathy (DR) OCTA
REFUGE: Retinal Fundus Glaucoma Challenge Welcome to the Retinal Fundus Glaucoma Challenge! REFUGE was organized as a half day Challenge in conjunction with the 5th MICCAI Workshop on Ophthalmic Medical Image Analysis (OMIA), a Satellite Event of the MICCAI 2018 conference in Granada, Spain. The goal of the challenge is to evaluate and compare automated algorithms for glaucoma detection and optic disc/cup segmentation on a common dataset of retinal fundus images. With this challenge, we made
Fundus Image Segmentation Benchmark Dataset for Blood Vessel Segmentation This benchmark dataset was created by combining three widely-used datasets: CHASEDB1, STARE, and DRIVE, to evaluate and enhance the performance of blood vessel segmentation models in fundus images. The dataset includes 28 high-resolution images from CHASEDB1, 20 diverse images from STARE, and 40 images from DRIVE, all preprocessed for uniformity in terms of size, contrast, and illumination. The data was split into
MMRDR Multi-Modal Retinal images for in-depth DR analysis (MMRDR)Diabetic Retinopathy (DR) is a leading cause of preventable blindness worldwide, particularly among working-age adults. We present a multi-modal retinal images dataset, comprising 11,118 CFP images, 10,404 UWF images and 2,938 OCT images. CFP and UWF images are annotated with 5 DR severity grades and 7 DR-related lesions, while OCT images are annotated with 3 DME severity classes. Ophthalmology
Dataset for calculating lattice vectors (and anisotropy) of zebrafish cone mosaic This dataset contains images of UV cone nuclei near the retinal margin in live zebrafish. These UV cone nuclei are labelled by transgenic expression of a fluorescent reporter (that is photoconvertible).

The most important data are:
1. The zoomed-in (4X magnification) images of UV cone nuclei immediately after photoconversion
2. The zoomed-in (4X magnification) images of UV cone nuclei 2-4 days after
OMAP-12: Organ Mapping Antibody Panel (OMAP) for Multiplexed Antibody-Based Imaging of Human Retina with IBEX, v1.2 OMAP-12 was developed for IBEX (Iterative Bleaching Extends multi-pleXity) imaging of fixed frozen human retina
samples (Radtke et al. 2022; 2020). The panel includes 14 antibodies, 3 lectins, and DAPI for nuclear segmentation and
image alignment. It captures spatial information for key anatomical structures and cell types in the healthy human
retina, such as
ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model Retinal OCT-Angiography Vessel Segmentation Dataset (ROSE) is now open source. It includes two subsets: ROSE-1 and ROSE-2.

ROSE-1 subset consists of a total of 117 OCTA images from 39 subjects (26 with disease and the rest are healthy control) and was split into 90 images for training and 27 images for testing. Each subject has en face angiograms of superficial (SVC), deep (DVC), and the inner retinal vascular plexus
MAPLES-DR MAPLES-DR is a dataset containing new diagnoses for Diabetic Retinopathy (DR) and Macular Edema (ME) as well as new pixel-wise segmentation maps for 10 anatomical and pathological biomarkers related to DR. Diabetic Retinopathy Fundus Images Macular Edema Segmentation Maps
CornOrb: A Multimodal Dataset of Orbscan Corneal Topography and Clinical Annotations for Keratoconus Detection This dataset contains 1,454 eyes from 744 patients (889 normal, 565 keratoconus) collected with the Orbscan 3 device in Algerian ophthalmology centers. For each eye, four corneal maps are provided (Axial, Anterior, Posterior, Pachymetry), alongside structured clinical annotations (age, sex, astigmatism, K max, pachymetry, asphericity, diagnostic label). All data are anonymized and
Code from: The retinal age gap: An affordable and highly accessible biomarker for population-wide disease screening across the globe Traditional biomarkers, such as those obtained from blood tests, are
essential for early disease detection, improving health outcomes, and
reducing healthcare costs. However, they often involve invasive
procedures, specialized laboratory equipment, or special handling of
biospecimens. The retinal age gap (RAG) has emerged as a promising new
biomarker that can
FOVEA: Preoperative and intraoperative fetinal fundus images with optic disc and retinal vessel annotations The performance and scope of computer vision methods applied to ophthalmic images is highly dependent on the availability of labelled training data. While there are a number of colour fundus photography datasets, FOVEA is to the best of our knowledge the first dataset that matches high-quality annotations in the intraoperative domain with those in the preoperative one. It comprises data
10-year Progression of Diabetic Retinopathy. Identification of Signs and Surrogate outcomes (PROGRESS 10) PROGRESS 10, 10-year Progression of Diabetic Retinopathy: Identification of Signs
and Surrogate Outcomes (https://clinicaltrials.gov/study/NCT04650165). It is a
continuation of the PROGRESS study in which the included participants are monitored for an additional 5-year period. This Dataset consists of demographic and diagnostic data of the participants selected for analysis in open
A Multimodal Dataset of Orbscan Corneal Topography and Clinical Annotations for Keratoconus Detection This dataset contains 1,454 eyes from 744 patients (889 normal, 565 keratoconus) collected with the Orbscan 3 device in Algerian ophthalmology centers. For each eye, four corneal maps are provided (Axial, Anterior, Posterior, Pachymetry), alongside structured clinical annotations (age, sex, astigmatism, K max, pachymetry, asphericity, diagnostic label). All data are anonymized and pre-processed
CORN-Pro To the best of our knowledge, no publicly available dataset exists for the segmentation of Langerhans cells (LCs) or stromal cells (SCs). To address this gap, we have extended the CORN dataset by introducing a new subset, CORN-4, specifically designed for segmentation tasks involving LCs and SCs. CORN-4 consists of 1,120 corneal confocal microscopy (CCM) images captured using the Heidelberg Retina Tomography System with the Rostock Cornea Module (HRT-III). The dataset includes 560
Human Embryonic Stem Cell-Derived Retinal Organoid Early Differentiation Imaging Dataset This multimodal time-series imaging dataset captures the early differentiation of human embryonic stem cell (hESC)–derived retinal organoids. It includes images documenting morphological development from Day 6 to Day 30 (post-seeding of the hESC suspension), acquired from 384 parallel-cultured cellular aggregates. Bright-field images are provided via the repository link; volumetric optical coherence
Preoperative estimation of retinal hole location using ultra-wide-field imaging Accurate localization of retinal holes is essential for successful scleral buckling (SB) surgery. We aimed to verify the feasibility of using ultra-wide-field (UWF) imaging for preoperative estimation of retinal hole location. We observed 21 eyes from 21 patients with rhegmatogenous retinal detachment (RRD) who underwent successful SB. They were treated at the Department of Ophthalmology of the Second Hospital of
Quantifying frequency content in cross-sectional retinal scans of diabetics vs. controls Purpose: To examine subtle differences in the structure of diabetic vs. control retinas.
Methods: Spectral-domain optical coherence tomography (SD-OCT) images were compared for the retinas of 33 diabetic subjects who did not have clinical evidence of diabetic macular edema and age-matched controls, with central macular thicknesses of 275 and 276 microns, respectively. Cross-sectional retinal images through
Iterative nick thresholding region growing for hemorrhage and exudate detection and segmentation to prescreen diabetic retinopathy Diabetic retinopathy (DR) is a prevalent eye condition affecting approximately one-third of individuals with diabetes, leading to vision loss in both working-age adults and the elderly. Early detection and intervention are crucial for improving patient outcomes and reducing the strain on healthcare systems. Developing advanced computational techniques enables the
Data for: Retinal layer segmentation in rodent OCT images: Local intensity profiles & fully convolutional neural networks Database of rodent OCTs composed of two batches of images. Animal experiment permission was granted by the Danish Animal Experimentation Council (license number: 2017-15-0201-01213). Rat OCT images were taken with the Micron IV equipment (Phoenix Research Labs, Pleasanton, USA). All rats were anesthetized previously to image acquisition. A set of 22 Sprague-Dawley rats
CORN-Complex CORN-Complex is a large-scale, multi-center, multi-disease dataset designed to reflect real-world clinical complexity. It comprises 7,522 images from 518 patients across 12 distinct corneal pathologies. Data were collected from six clinical centers using two imaging systems: Confoscan 3.0 (resized to 384×384) and Heidelberg HRT-III/RCM (384×384). All images were exported directly from CCM devices without manual quality filtering to preserve the full spectrum of variability
Ultra-wide-field (SLO) fundus image dataset for intelligent diabetic retinopathy system Diabetic retinopathy (DR), the most prevalent microvascular complication of diabetes mellitus, is the leading cause of irreversible vision loss in the global working-age population. At present, deep learning-integrated ultra-wide-field (UWF) image analysis systems have improved DR grading consistency and reduced peripheral lesion misdiagnosis rates, thereby overcoming the limitations of traditional
In Vivo Analysis and Comparison of Anterior Segment Structures of Both Eyes in Unilateral Herpetic Anterior Uveitis ABSTRACT Purpose: To compare the Schlemm’s canal area (SCA) and to define iris features in patients with unilateral herpetic anterior uveitis (HAU) by using anterior segment spectral-domain optical coherence tomography (AS SD-OCT). Methods: Unilateral HAU cases that had been in complete remission for ≥ 3 months were included. Two investigators analyzed the iris features of HAU.
Progression of Diabetic Retinopathy. Identification of Signs and Surrogate outcomes (PROGRESS) PROGRESS, Progression of Diabetic Retinopathy. Identification of Signs and
Surrogate outcomes (https://clinicaltrials.gov/study/NCT03010397) is a clinical study conducted between 2016 and 2019. This study consisted of both a retrospective and prospective phases to follow up the enrolled subjects for five years. The objective of the study was to characterize both functionality and morphology of the
Replication data for: Human Foveal Cone and RPE Cell Topographies and Their Correspondence With Foveal Shape DATASET MIGRATED FROM FIGSHARE:

Abstract

Purpose: To characterize the association between foveal shape and cone and retinal pigment epithelium (RPE) cell topographies in healthy humans.

Methods: Multimodal adaptive scanning light ophthalmoscopy and optical coherence tomography (OCT) were used to acquire images of foveal cones,
Cirrus HD-OCT repeatability dataset.tif Repeatability dataset comprising OCT (20°x20°; 512x128) images obtained on 25 participants, at two separate imaging sessions, using a Zeiss Cirrus HD-OCT. 111399 Ophthalmology and Optometry not elsewhere classified FOS: Clinical medicine FOS: Clinical medicine