fairdataihub/envision-eye-imaging-training-data huggingface dataset:
Characterization of Retinal Vascular Disease in Eyes With Mild to Moderate NPDR in Diabetes Type 2, using novel non-invasive Imaging methods, in a longitudinal, prospective and interventional Clinical CORDIS, Characterization of Retinal Vascular Disease in Eyes With Mild to Moderate
NPDR in Diabetes Type 2 (https://clinicaltrials.gov/study/NCT03696810), is a clinical
study that was conducted between 2018 and 2020. The primary objective of the study was to characterize the main alterations of
Rotterdam Artery-Vein Segmentation Dataset - RAV 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. The public / open
Supplementary Material for: Comparison of 55° Wide-Field Spectral Domain Optical Coherence Tomography and Conventional 30° Optical Coherence Tomography for the Assessment of Diabetic Macular Edema Purpose: To compare conventional 30° spectral domain optical coherence tomography (SD-OCT) with 55° wide-field SD-OCT for the assessment of diabetic macular edema (DME). Methods: This study included 50 DME patients. Both 55° and 30° SD-OCT was conducted. Two readers
RIGA+ Dataset for Unsupervised Domain Adaptation in Medical Image Segmentation Different from the previous combined multi-domain dataset for unsupervised domain adaptation (UDA) in medical image segmentation, this multi-domain fundus image dataset contains annotations made by the same group of ophthalmologists. Hence the annotator bias among different datasets can be mitigated. Therefore, this dataset can provide a relatively fair benchmark for evaluating UDA methods in fundus image
Retina OCT glaucoma This data set is part of the public development data for the 2023 Automated Universal Classification Challenge (AUC23). The data set was previously introduced and described by Ishikawa, H. (2018) and concerns the classification of eyes as healthy or glaucomatous from optical coherence tomography (OCT) volumes of the optic nerve head. Data was restructured in compliance with the AUC23 challenge format, and no images or patient information were added. The data set consists of
MTNet-datasets.rar A controlled experiment was conducted to test the performance of the CSP_UNet algorithm. In this paper, four commonly used retinal fundus image datasets with a total of 133 images were selected, containing DRIVE, STARE , CHASE_DB1 and HRF.The diabetic retinopathy dataset was derived from the data of the Kaggle Aptos2019 competition, and the diabetic retinopathy in each image was rated on a scale of 0 to 4: 0-normal (no DR), 1-mild, 2-moderate, 3-severe, and 4-proliferative
Evaluation of fundus autofluorescence ımaging of diabetic patients without retinopathy ABSTRACT Purpose: To evaluate the usefulness of fundus autofluorescence imaging of diabetic patients without retinopathy to investigate early retinal damage. Methods: Fundus autofluorescence images of patients with type 2 diabetes mellitus without retinopathy (diabetic group) and age-sex matched healthy patients (control group) were recorded with a CX-1 digital mydriatic retinal camera after detailed
Dataset used in toward ground-truth optical coherence tomography 3D datasets used in Toward-ground-truth optical coherence tomography. Guangming Ni et al., "Toward ground-truth optical coherence tomography via three-dimensional unsupervised deep learning processing and data", 2023 There are two dataset: OCT-R1 and OCT-R2. OCT-R1 contains three-dimensional (3D) data collected from 41 human eyes using a BM-400K BMizar (Topi Ltd.) OCT scanner at Sichuan Provincial People's Hospital. To enhance the
Supplementary Material for: A Combined Index of Steady-state Pattern Electroretinogram and Optical Coherence Tomography improved the Detection of Early Glaucoma Introduction: To evaluate the structure-function relationship between steady-state pattern electroretinogram (ssPERG), optical coherence tomography (OCT), and visual field (VF) tests, and to investigate indicators that enhance the detection of preperimetric and early-stage primary open angle glaucoma (POAG).
Methods: In this
HPMI: A retinal fundus image dataset for identification of high and pathological myopia based on deep learning Myopia is one of the leading causes of visual impairment worldwide and can progress to high or pathological myopia (HM or PM) if proper measures were not taken. Accurate identification of HM and PH plays an important role for their intervention and treatment, which can be implemented leveraging deep learning technology on sufficient annotation image data. However, few efforts have been
Test UWF image datasets BackgroundRetinal breaks are critical lesions that can lead to retinal detachment and vision loss if not detected and treated early. Automated and precise delineation of retinal breaks using ultra-widefield (UWF) fundus images remains a significant challenge in ophthalmology.ObjectiveThis study aimed to develop and validate a deep learning model based on the PraNet architecture for the accurate delineation of retinal breaks in UWF images, with a particular
Diabetic Retinopathy and Retinal Diseases Fundus Image Dataset The dataset used in this study consists of 22,454 high-quality color fundus images from three publicly available sources, merged for the classification of diabetic retinopathy (DR) and other retinal diseases. The images are categorized into 13 clinically significant classes, including various stages of DR (Mild, Moderate, Severe, Proliferative, Advanced PDR, Very Severe NPDR), other retinal conditions like Dry AMD, Wet AMD,
OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods Optical coherence tomography (OCT) is a non-invasive imaging technique that has extensive clinical applications in ophthalmology. OCT enables the visualization of the retinal layers, playing a vital role in the early detection and monitoring of retinal diseases. OCT uses the principle of light wave interference to create detailed images of the retinal microstructures, making it a valuable tool for diagnosing
DR and AMD fundus image classification The dataset used in this paper for retinal diseases classification, including diabetic retinopathy (DR) and age-related macular degeneration (AMD). age-related macular degeneration diabetic retinopathy fundus image classification retinal diseases
"Fundus Image Dataset for Five-Stage Diabetic Retinopathy Classification" "Diabetic retinopathy (DR) is a leading cause of vision impairment worldwide, and accurate automated detection is critical for early intervention. We present a curated dataset of 6,399 retinal fundus images labeled across five DR severity levels: No DR (2,805 images), Mild (740), Moderate (1,898), Proliferative Mild (590), and Severe (366). The dataset captures diverse retinal features and class imbalances typical of
OCT5k: A dataset of multi-disease and multi-graded annotations for retinal layers 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
Dataset Infectious keratitis is a group of corneal disorders in which corneal tissues suffer inflammation and damage caused by pathogenic infections. Among these, Fungal Keratitis (FK) and Acanthamoeba Keratitis (AK) are some of the most severe, with a high chance of permanent blindness if they are not diagnosed early and accurately. In Vivo Confocal Microscopy (IVCM) allows the imaging of different layers of the cornea, providing an important tool for an early and more accurate diagnosis. In
RVO-Lesion: A Dual-Task OCT Dataset for Joint Segmentation and Detection of Macular Lesions in Retinal Vein Occlusion Retinal vein occlusion (RVO) is one of the most common vision-threatening retinal diseases, primarily due to complications such as macular edema (ME). Optical coherence tomography (OCT), a non-invasive imaging modality, has become an essential tool for the diagnosis, treatment monitoring, and clinical evaluation of RVO, owing to its ability to clearly visualize fine retinal
Digital Retinal Images for Vessel Extraction (DRIVE) The Digital Retinal Images for Vessel Extraction (DRIVE) database contains 40 2D color fundus images of retinal vessels with a size of 584 × 565, including 20 images for training and the rest 20 images for testing. fundus images medical imaging retinal vessels
OCT Images Dataset Description:

Retinal Optical Coherence Tomography (OCT):

OCT Images Dataset is a non-invasive imaging technique that generates high-resolution cross-sectional images of the retina. This method is critical for diagnosing and monitoring retinal conditions by allowing clinicians to examine the internal structure of the retina in great detail. According to estimates, around 30 million OCT scans are conducted annually worldwide, underscoring the importance of
Supplementary Material for: Evaluation of an Artificial Intelligence-based Detector of Sub- and Intra-Retinal Fluid on a large set of OCT volumes in AMD and DME Abstract Introduction: In this retrospective cohort study, we wanted to evaluate the performance and analyze the insights of an artificial intelligence (AI) algorithm in detecting retinal fluid in spectral-domain OCT volume scans from a large cohort of patients with neovascular age-related macular degeneration (AMD) and diabetic macular
Imaging the retinal and choroidal vasculature using Spatio-Temporal Optical Coherence Tomography (STOC-T) Spatio-Temporal Optical Coherence Tomography (STOC-T) is a novel imaging technique using light with controlled spatial and temporal coherence. Retinal images obtained using the STOC-T system maintain high resolution in all three dimensions, on a sample of about 700 μm, without the need for mechanical scanning. In the present work, we use known data processing algorithms for optical
Aptos dataset The Aptos dataset is a collection of 705 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. Aptos dataset medical diagnosis retinal images
PRIME-FP20: Ultra-Widefield Fundus Photography Vessel Segmentation Dataset PRIME-FP20 dataset is established for development and evaluation of retinal vessel segmentation algorithms in ultra-widefield fundus photography. PRIME-FP20 provides 15 high-resolution ultra-widefield fundus photography images acquired using the Optos 200Tx camera (Optos plc, Dunfermline, United Kingdom), the corresponding labeled binary vessel maps, and the corresponding binary masks for the FOV of the images. Computer
Multi-task OCT for Octascope This dataset supports the development of Octascope, a high-performance foundation model for optical coherence tomography (OCT) image analysis. The dataset comprises 449,000+ OCT images spanning multiple tissue types, including retina, kidney, and abdominal tissues, curated from publicly available sources and prior research efforts. These images were used in a curriculum learning strategy to pre-train convolutional neural networks on both general (ImageNet) and
Supplementary Material for: Comparison of multicolor scanning laser imaging and color fundus photography in evaluating vessel whitening in branch retinal vein occlusion Introduction: Few studies have explored Multicolor™ imaging (MCI) in evaluating retinal vascular diseases, particularly branch retinal vein occlusion (BRVO). This study aimed to compare the identification of retinal vessel whitening in BRVO using MCI by scanning confocal laser versus conventional white flash color fundus
Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation This dataset contains 1200 color fundus images, which are a powerful resource to observe the fundus lesions, and build tools for automated diagnosis and analysis of pathologic myopia. In addition to color fundus images and labels for pathologic myopia category, our pathologic myopia (PALM) dataset also provides manual annotations of the optic disc and two lesion regions (atrophy and
Quantitative analysis of optical coherence tomography for neovascular age-related macular degeneration using deep learning Purpose: To apply a deep learning algorithm for automated, objective, and
comprehensive quantification of optical coherence tomography (OCT) scans
to a large real-world dataset of eyes with neovascular age-related macular
degeneration (AMD), and make the raw segmentation output data openly
available for further research. Design: Retrospective analysis of OCT
images
RetinaX dataset RetinaX dataset is built by selectively combining four publicly available datasets: the STARE dataset, ARIA dataset, RFMiD dataset, and RFMiD 2.0 dataset. It contains a total of 2,514 images and 24 distinct labels, covering nearly all common and rare retinal diseases. The label distribution and sample count are shown in the above figure, following a long-tail pattern, and includeing a wide range of disease status: DR (Diabetic Retinopathy), ODC (Optic Disc Cupping), MH (Media
LCFP-14M: 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
Replication Data for: Self-supervised contrastive learning improves machine learning discrimination of full thickness macular holes from epiretinal membranes in retinal OCT scans Compiled processed OCT scan data of FTMH+/- retinas for spatial contrastive learning using RaSCL. Medicine, Health and Life Sciences
Dual-Input Neural Networks for Personalized Image Precompensation This dataset supports the paper "Dual-Input Neural Networks for Personalized Image Precompensation" (J. Imaging, 2025).It includes color images and point spread functions (PSFs) used to train and evaluate deep neural networks that compensate for human visual distortions caused by refractive errors.

📂 Dataset structure:

train/

1000 color images from ImageNet-21k (resized to 512×512)

PSFs from three categories:
DIAMOND: The Database of Iowa Animal Models for Ophthalmic and Neurologic Disease. Dataset 1: Slit-lamp images for mouse models of exfoliation glaucoma. The following dataset is a collection of slit-lamp photographs of mice with various mutations relevant to genome-wide association studies (GWAS) of exfoliative glaucoma. Pseudoexfoliation syndrome Iris Glaucoma genetics Cornea Conjunctiva GWAS genetics Exfoliative glaucoma Agpat1 Cacna1a Loxl1
The human fovea is relatively horizontally elongated in infantile nystagmus Numerical data associated with the manuscript, MATLAB code to identify the deepest foveal scan from a stack of foveal OCT images, and OCT images of the deepest scans from study participants.
CIL:40262, Mus musculus, CNS neuron (sensu Vertebrata). In Cell Image Library Serial section reconstruction of the optic nerve head in the area of transition between myelinated and unmyelinated axons the mouse obtained through serial section block face 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 astrocytes are
HYAMD High-Resolution Fundus Image Dataset for age related macular degeneration (AMD) Diagnosis The Hillel Yaffe Age Related Macular Degeneration (HYAMD) longitudinal dataset
comprises of 1,560 Digital Fundus Images (DFIs) of 325 patients examined at
the Hillel Yaffe Medical Center (Hadera, Israel, Helsinki approval number
0048-24-HYMC) providing follow-up data from 2021 to 2024. The AMD cohort
includes 147 patients (73 males, 74 females) aged 54-94, while the control
group comprises 190
ExpertMatcher dataset The dataset used for the ExpertMatcher framework, which includes six datasets: STL-10, MNIST, HAR, Reuters, Non Line of Sight, and Diabetic Retinopathy. Autoencoders Distributed Computing FOS: Computer and information sciences ExpertMatcher Machine Learning
CAVRI-C: Vessels segmentation from OCT 3D scans This database contains 24 3D optical coherence tomography (OCT) scans of 24 healthy subjects, with an average age of 27. The images were collected using the Avanti RTvue device with a 3D Retina scanning protocol (volumetric scan with 141 cross-sections). This dataset also contains manually segmented retina vessels for the OCT-reconstructed fundus images.

This dataset was created as part of research conducted within the CAVRI (Computer Analysis of
Inter-eye Molecular Discrepancies in the Corneal Epithelium Point to TFRC in the Keratoconus Severity Signature and Mechanism of Cone Formation This dataset accompanies the manuscript titled 'Inter-eye Molecular Discrepancies in the Corneal Epithelium Point to TFRC in the Keratoconus Severity Signature and Mechanism of Cone Formation’ by Jaskiewicz-Rajewicz et al., submitted to Investigative Ophthalmology & Visual Science (IOVS).
The data was processed on 10 July 2025.
Mendeley
SynthesEyes Dataset Images of the eye are key in several computer vision problems, such as shape registration and gaze estimation. Recent large-scale supervised methods for these problems require time-consuming data collection and manual annotation, which can be unreliable. We propose synthesizing perfectly labelled photo-realistic training data in a fraction of the time. We used computer graphics techniques to build a collection of dynamic eye-region models from head scan geometry. These were
Supplementary Material for: Evaluation of Reticular Pseudodrusen by Polarization-Sensitive Optical Coherence Tomography: case report Introduction: Reticular pseudodrusen (RPD) are an important clinical biomarker associated with progression to late age-related macular degeneration (AMD). We report polarization-sensitive optical coherence tomography (PS-OCT) findings in a case of RPD and evaluate the entropy characteristics of these lesions.
Case Presentation: A 73-year-old man presented for
Spatio-Temporal Optical Coherence Tomography provides full thickness imaging of the chorioretinal complex Despite the rapid development of optical imaging methods, high-resolution in vivo imaging with penetration into deeper tissue layers is still a major challenge. Optical
Coherence Tomography (OCT) has been used successfully for non-invasive human retinal volumetric imaging in vivo , advancing the detection, diagnosis, and monitoring of various retinal diseases. However, there are important
AMDNet23: Fundus Image Dataset for Age-Related Macular Degeneration Disease Detection AMDNet23 is a curated dataset of 2000 high-quality fundus images compiled from six public sources: Ocular Disease Recognition, DR_200, Fundus Dataset, RFMiD, HRF, and ARIA. It includes 4 balanced classes with 400 images each: Normal, Diabetes, Cataract, and Age-related Macular Degeneration (AMD). All images were enhanced using advanced preprocessing techniques (Gamma correction, CLAHE and so on) to improve
Automated Quantification of Macular Perfusion in Behçet’s Uveitis Using Optical Coherence Tomography Angiography This study created an algorithm to quantify the vascular density of the capillary, non-capillary, and total vessels in deep and superficial layers of retina by using optical coherence tomography angiography (OCT-A) on Behçet’s Uveitis (BU) patients to assess the severity and prognosis of BU to help clinicians. Medicine Behçet’s disease Uveitis Optical coherence tomography angiography
Diabetic Retinopathy Early Analysis and Detection System This preprint presents an AI-driven system for the early analysis and detection of Diabetic Retinopathy (DR) using ensemble deep learning techniques. Diabetic Retinopathy is a leading cause of preventable blindness worldwide, and timely diagnosis plays a critical role in reducing irreversible vision loss.

The proposed framework integrates multiple deep learning architectures, including convolutional neural networks and transformer-based
Supplementary Material for: Evaluation of Retinal Pigment Epithelium and Choroidal Neovascularization in Rats Using Laser-Scanning Optical-Resolution Photoacoustic Microscopy Purpose: To demonstrate the value of the laser-scanning optical-resolution (LSOR)-photoacoustic (PA) microscopy (PAM) system and the conventional multimodal imaging techniques in the evaluation of laser-induced retinal injury and choroidal neovascularization (CNV) in rats. Methods: Different
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 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 &
Dataset of ocular wavefront vergence coefficients and PTV-normalized metrics in myopic eyes 2013–2025 This record provides a de-identified dataset of ocular wavefront–derived vergence coefficients in myopic candidates for corneal refractive surgery examined at Fondation Rothschild (Paris, France) between 2013 and 2025. Measurements were acquired with the OPD-Scan III (Nidek) using automated spatial dynamic skiascopy and centered on the corneal vertex. Inclusion criteria for this public release
OCTA-500 Optical coherence tomography angiography (OCTA) is a novel imaging modality that allows a micron-level resolution to present the three-dimensional structure of the retinal vascular.We propose a new multi-modality dataset, dubbed OCTA-500. It contains 500 subjects with 2 field of view (FOV) types, including OCT and OCTA volumes, 6 types of projections, 4 types of text labels and 2 types of pixel-level labels. This dataset contains more than 360K images with a size of about 80GB. Now,
Dataset underlying the study: Combined Diagnostic Accuracy of Two Artificial Intelligence Software for Glaucoma Diagnosis Using Color Fundus Photography This Excel spreadsheet contains the database used to select the patients studied in the manuscript entitled above. It is divided into 4 parts: clinical data (white background); visual field data (yellow background); optical coherence tomography data (peripapillary retinal nerve fiber layer in light green background and macular ganglion cell
OMAP-12: Organ Mapping Antibody Panel (OMAP) for Multiplexed Antibody-Based Imaging of Human Retina with IBEX, v1.1 OMAP-12 was designed for IBEX imaging of fixed frozen human retina samples
(https://doi.org/10.1038/s41596-021-00644-9; https://doi.org/10.1073/pnas.2018488117). The panel contains 14
antibodies, 3 lectins, and DAPI for image alignment and nuclear segmentation. The OMAP provides a spatial context for
the main anatomical structures and key cell types present in the healthy
OMAP-12: Organ Mapping Antibody Panel (OMAP) for Multiplexed Antibody-Based Imaging of Human Retina with IBEX, v1.0 OMAP-12 was designed for IBEX imaging of fixed frozen human retina samples
(https://doi.org/10.1038/s41596-021-00644-9; https://doi.org/10.1073/pnas.2018488117). The panel contains 14
antibodies, 3 lectins, and DAPI for image alignment and nuclear segmentation. The OMAP provides a spatial context for
the main anatomical structures and key cell types present in the healthy