fairdataihub/envision-eye-imaging-training-data huggingface dataset:
Ex vivo multispecies dataset for three-dimensional reconstruction of the corneal stromal nerve plexus using hexapod-based confocal microscopy and deep-learning segmentation This dataset provides three-dimensional confocal microscopy images and deep-learning-based nerve segmentations of the stromal corneal nerve plexus from ex vivo samples of multiple species (porcine, murine, ovine, and human). Corneal volumes were acquired using a confocal microscope synchronized with a programmable six-axis
Deep learning for predicting uncorrected refractive error using posterior segment optical coherence images ***A sample anonymized OCT data with and without high myopia is available in this source. Note that this was not the exact data used in the research, but is a cleaned-up reproduction of our study's key insight. Background/Aims: The purpose of this study is to evaluate the deep learning model for estimating uncorrected refractive error using posterior segment OCT images. Definition of
Deep learning for predicting uncorrected refractive error using posterior segment optical coherence tomography images Article title: Deep learning for predicting uncorrected refractive error using posterior segment optical coherence tomography images
Journal: Published in "EYE"
DOI: 10.1038/s41433-021-01795-5
5 May 2021 / Accepted: 24 September 2021

***A sample anonymized OCT data with and without high myopia is available in this source. Note that this was not the exact data used in the
Ocular Image Dataset for Eye Disease Classification and Screening Research Dataset Overview

This dataset is designed for eye disease classification and research purposes. It contains labeled ocular images representing five clinically relevant categories: Cataract, Conjunctivitis, Eyelid Disorders, Uveitis, and Normal (healthy eyes). The dataset aims to support research in computer aided diagnosis, medical image analysis, and artificial intelligence based ophthalmic applications. It is suitable
Choroidal thickness measurement in healthy pediatric population using Cirrus HD optical coherence tomography Purpose: To investigate the association between central choroidal thickness (CT), axial length (AL), age, gender, and refractive error in a healthy pediatric population using optical coherence tomography (OCT). Methods: This institutional study involved 137 healthy children (57 boys, 80 girls) aged between 4 and 18 years. Each child underwent a dilated eye examination, cycloplegic
Dataset: FAIR AMD OCT Datasets Paper This is the dataset associated with the paper titled "Publicly Available Imaging Datasets for Age-related Macular Degeneration: Evaluation according to the Findable, Accessible, Interoperable, Reproducible (FAIR) Principles". Age-related macular degeneration (AMD), a leading cause of vision loss among older adults, affects more than 200 million people worldwide. In this paper, We evaluated openly available AMD-related datasets containing optical coherence
Data from: Human retinal pigment epithelium: in vivo cell morphometry, multispectral autofluorescence, and relationship to cone mosaic Purpose: To characterize in vivo morphometry and multispectral
autofluorescence of the retinal pigment epithelial (RPE) cell mosaic and
its relationship to cone cell topography across the macula. Methods: RPE
cell morphometrics were computed in regularly spaced regions of interest
(ROIs) from contiguous short-wavelength autofluorescence (SWAF) and
Optical coherence tomography database Optical coherence tomography (OCT) is an emerging technology for performing high-resolution cross-sectional imaging. It utilizes the interferometry concept to create a cross-sectional map of the retina. OCT images are two-dimensional data sets that represent the optical backscattering in a cross-sectional plane through the tissue. OCT allows non-secondary visualization of various structures of the eye, including the retina, vitreous body, and choroid,
Annotated Infant Fundus Images for Object Detection of Key Structures (Optic Disk and Macula) The data set contains fundus images for object detection research.
Some images have clipped corners (patient info watermark is printed in this area) to ensure they are de-identified.
All images are annotated by one senior pediatric ophthalmologist in SEH (Shenzhen Eye Hospital).
An image annotation tool is developed based on the "VGG Image Annotator (VIA)" tool from the University of Oxford.The tool is
M3-OCTA:Leveraging Multimodal Fusion for Enhanced Diagnosis of Multiple Retinal Diseases in Ultra-wide OCTA Ultra-wide optical coherence tomography angiography (UW-OCTA) is an emerging imaging technique that offers significant advantages over traditional OCTA by providing an exceptionally wide scanning range of up to 24 x 20 mm^{2}, covering both the anterior and posterior regions of the retina. However, the currently accessible UW-OCTA datasets suffer from limited comprehensive hierarchical
Data for: Improved accuracy in OCT diagnosis of rare retinal disease using few-shot learning with generative adversarial networks The retinal OCT images ofr rare diseases were extracted by using the Google image and Google dataset search that included English keywords including central serous chorioretinopathy (CSC), macular telangiectasia (MacTel), macular hole (MH), Stargardt disease, retinitis pigmentosa (RP). These rare diseases were selected according to a previous review article about OCT
Topcon 3D-OCT 1000 repeatability dataset.tif Repeatability dataset comprising OCT (20°x20°; 512x128) images obtained on 25 participants, at two separate imaging sessions, using a Topcon 3-D OCT 1000. 111399 Ophthalmology and Optometry not elsewhere classified FOS: Clinical medicine FOS: Clinical medicine
Retinal fundus images for glaucoma analysis: the RIGA dataset The dataset includes 3 different files: 1) MESSIDOR dataset file contains 460 original images and 460 images for every single ophthalmologist manual marking in total of 3220 images for the entire file. 2) Bin Rushed Ophthalmic center file and contains 195 original images and 195 images for every single ophthalmologist manual marking in total of 1365 images for the entire file. 3) Magrabi Eye center file and contains 95 original
Robust semi-automatic vessel tracing in the human retinal image by an instance segmentation neural network Vasculature morphology and hierarchy are essential for blood perfusion.
Human retinal 20 circulation is an intricate vascular system emerging and
remerging at the optic nerve head 21 (ONH). Tracing retinal vascular
branching from ONH can allow detailed morphological 22 quantification, and
yet remains a challenging task. We presented a robust semi-automatic 23
vessel tracing algorithm
FOCA:Fine-Grained OCTA dataset focuses on the complete microvascular structure These data are from paired Angiovue (RTVue XR Avanti, Optovue) instrument and Zeiss Cirrus 5000-HD-OCT Angioplex (Carl Zeiss Meditec) instrument devices, totaling 88 images(1:1).

Dataset
Range
Detail
Number

FOCA(with annotation)
3mm*3mm
These 40 paired images were labeled using a complete fine annotation that was able to label each identifiable microvascular region. The labeling process is integrated according
DIAMOND: The Database of Iowa Animal Models for Ophthalmic and Neurologic Disease. Dataset 2: Slit-lamp images for transgenic mice with the human LOXL1/LOXL1-AS1 locus associated with exfoliation glau The following dataset is a collection of slit-lamp photographs of mice in which 166 kb of human genomic DNA from the 15q24.1 locus (BAC RP11-71M11) was introduced into the mouse genome via BAC transgenes. Exfoliative glaucoma Pseudoexfoliation syndrome Iris Cornea Anterior chamber Glaucoma
SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation The proposed SA-UNet is evaluated on two publicly available retinal fundus image datasets: DRIVE and CHASE_DB1. deep learning medical image analysis retinal vessel segmentation
Supporting data for "Application of Retinal-nerve-fiber-layer Optical Texture Analysis (ROTA) in Glaucoma" Glaucoma is the leading cause of irreversible blindness characterised by chronic degeneration of retinal ganglion cells (RGCs). However, the diagnosis and monitoring of glaucoma is sometimes challenging, especially in late stages of disease. Retinal-nerve-fiber-layer optical texture analysis (ROTA) is an algorithm that combines the thickness and reflectance measurements of RNFL obtained
Handcrafted features for fundus image classification This dataset contains the hand-crafted features extracted for a number of images originating from the following public datasets: Kaggle DR and Messidor datasets,Indian Diabetic Retinopathy Image Dataset (IDRiD). 80104 Computer Vision FOS: Computer and information sciences
Repeatability and agreement of corneal thickness measurements by three methods of pachymetry in small incision lenticule extraction eyes Background: The accurate evaluation of corneal thickness (CT) post small incision lenticule extraction (SMILE) is clinically relevant to reduce the risk of complications. We aimed to analyze repeatability and agreement of central corneal thickness (CCT), thinnest corneal thickness (TCT), and mid-peripheral corneal thickness (MPCT) measurements using
Repeatability and Reproducibility of Slit Lamp, Optical Coherence Tomography, and Scheimpflug Measurements of Corneal Scars Purpose: To determine the repeatability and reproducibility of anterior segment optical coherence tomography (AS-OCT) and Scheimpflug photography for several measurements of corneal scars, including scar size, scar depth, and corneal thickness. Methods: A series of patients treated for fungal keratitis at a tertiary eye care center in South India were
Supporting Data for Thesis Titled "Harnessing the Power of Deep Learning for Ophthalmic Disease Diagnosis: A Study on Glaucoma and Exudative Age-Related Macular Degeneration " The dataset comprises optical nerve head radial peripapillary capillaries (ONH RPC) angiography images captured using a commercially available spectral-domain Zeiss Cirrus 6000 HD OCT (Carl Zeiss AG, Oberkochen, Germany). The OCTA scans cover a 4.5 mm x 4.5 mm area around the optic nerve head, with an A-scan rate
Predicting incremental and future visual change in neovascular age-related macular degeneration using deep learning Purpose To evaluate the predictive utility of quantitative imaging
biomarkers, acquired automatically from optical coherence tomography (OCT)
scans, of cross-sectional and future visual outcomes of patients with
neovascular age-related macular degeneration (AMD) starting anti-vascular
endothelial growth factor (VEGF) therapy. Design Retrospective cohort
study.
3D OCT Images of Murine Uveitis Images were obtained from the eyes of anaesthetised mice using the Micron IV retinal imaging microscope. Spectral domain optical coherence tomography (OCT) images were obtained as 512 serial B-scans of a volume of tissue centred on the optic nerve head. Prior to imaging the Micron IV was fully calibrated according to the manufacturer’s protocol. Images (512x1024 pixels) with a bit depth of 16 were acquired using PackBits lossless compression. Experimental
A Composite Retinal Fundus and OCT Dataset along with Detailed Clinical Markings for Extracting Retinal Layers, Retinal Lesions and Screening Macular and Glaucomatous Disorders This repository contains composite retinal fundus and OCT dataset for analyzing retinal layers, retinal lesions, and to diagnose normal and abnormal retinal diseases like Macular Edema, Central Serous Retinopathy, Age-related Macular Degeneration, and Glaucoma. Ophthalmology Retina Glaucoma Central Serous
Repeatability and Reproducibility of Anterior Chamber Angle Measurement with Swept-Source Optical Coherence Tomography in Patients with Primary Angle Closure Suspect Purpose: To evaluate the inter- and intra-observer reliability of anterior chamber (AC) angle measurements obtained by swept-source optical coherence tomography (SS-OCT). Methods: Forty-eight consecutive patients diagnosed with primary angle closure suspect (PACS) were included. Three masked observers at different
Examples of misclassified images of papilledema severity by the Deep Learning System The study's objective is to evaluate the performance of a deep
learning system (DLS) in classifying the severity of papilledema
associated with increased intracranial pressure, on standard retinal
fundus photographs. A DLS was trained to automatically classify
papilledema severity in 965 patients (2103 mydriatic fundus photographs),
representing a multiethnic cohort of patients with confirmed elevated
Data from: Plane wave Doppler determination of blood flow in retinopathy of prematurity Retinopathy of prematurity (ROP) is a disorder affecting low birthweight,
preterm neonates. In the preterm eye, the retina is not fully developed
and neovascularization may occur at the margin between the developed
vascular retina and undeveloped avascular retina. Without timely treatment
by laser or intravitreal anti-vascular endothelial growth factor (VEGF)
therapy, this can lead to tractional retinal
Development and Validation of a Deep Learning Model for Macular Hole Staging from Optical Coherence Tomography Objective: To explore the use of artificial intelligence (AI) deep learning algorithms to construct an automated identification model for macular hole (MH) staging and intelligent assisted diagnosis and treatment system based on fundus optical coherence tomography (OCT) images. Method: Based on the image data of macular hole diagnosed by OCT examination at Sichuan Eye Hospital from
Retinal Vessel Three real-world public retinal datasets:

1. DRIVE Ref. Ridge-based Vessel Segmentation in Color Images of The Retina (https://drive.grand-challenge.org/Download)

2. STARE Ref. Locating Blood Vessels in Retinal Images by Piecewise Threshold Probing of a Matched Filter Response (https://cecas.clemson.edu/~ahoover/stare)

3. CHASE_DB1 Ref. An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation (https://blogs.kingston.ac.uk/retinal/chasedb1/)
Dataset for analyzing Y-junction motion in live fish retinae This dataset contains images of UV cone nuclei (labelled by transgenic expression of a photoconvertible fluorescent protein) near the retinal margin in live fish.

The most important images in the dataset are the following:
1. Images (at 4X magnification) of UV cones immediately after photoconversion of a patch near the retinal margin
2. Images (at 4X magnification) of UV cones 2-4 days after photoconversion of a patch near the
A CycleGAN deep learning technique for artifact reduction in fundus photography Herein, we present a deep learning technique to remove artifacts automatically in fundus photograph. By using a CycleGAN model, we synthesize the retinal images with artifact reduction based on low-quality image, and validated this technique in the independent test dataset. This study included total 2,206 anonymized retinal images. We collected the fundus photographs without qualification, which include normal and
Multimodal Eye Imaging, Retina Characteristics, and Psychological Assessment Dataset The eyes offer insights into to psychology, potentially providing a unique perspective for psychological health profiles. However, there existed a shortage of datasets that simultaneously encompass eye features and psychological assessments. To address this gap, our study presented a dataset that included Fundus Photography, Psychological Assessment, Retina Characteristics, and Multimodal Imaging (FPRM).
CIL:6518, Danio rerio, Outer plexiform layer of retina, Bipolar cell of retina. In Cell Image Library This study of the zebrafish eye is a series of four images taken at increasing magnifications. The first image is the SEM block face of the zebrafish eye. This is followed by a second image that is a higher magnification TEM image of the plexiform layer and the outer nuclear layer of the zebrafish retina. This image, the third image in the series, is a 15,000X TEM image of the bipolar and
Labelled Dataset of Retinal Images for Glaucoma detection Fundus photography is a viable option for glaucoma population screening. In order to facilitate the development of computer-aided glaucoma detection systems, we publish this annotation dataset that contains manual annotations of glaucoma features for seven public fundus image data sets. All manual annotations are made by a specialised ophthalmologist. For each of the fundus images in the seven fundus datasets, the upper, the bottom, the
Ridge-based vessel segmentation in color images of the retina Ridge-based vessel segmentation in color images of the retina. Color Images Retina Retinal Vessel Segmentation Ridge-based Segmentation Vessel Segmentation color images retinal vessel segmentation
Comparison of choroidal thickness in normal eyes and different ocular pathologies in a Brazilian population Abstract Objective: To evaluate the choroidal thickness (CT) in healthy Brazilian subjects using spectral-domain optical coherence tomography (SD-OCT) and to compare with choroidal thickness measured in Brazilian patients with diabetic macular edema (DME), neovascular age-related macular degeneration (AMD) and high myopia. Methods: A retrospective analysis of spectral domain optical
Supplementary Material for: Optical Coherence Tomography: Clinicopathologic Correlations - The 2016 Gordon K. Klintworth Lecture Background/Aims: Spectral domain optical coherence tomography (SD-OCT) has become a mainstay of ophthalmic practice that has revolutionized the assessment and treatment of a variety of ocular disorders. Studies that directly correlate SD-OCT and histopathology are necessary to demonstrate the actual nature of the retinal pathology responsible for the
Synthetic Medical Images for Robust, Privacy-Preserving Training of AI: Application to Retinopathy of Prematurity Diagnosis Purpose: Developing robust artificial intelligence (AI) models for medical image analysis requires large quantities of diverse, well-curated data, which can prove challenging to collect due to privacy concerns, disease rarity, or diagnostic label quality. Collecting datasets for training diagnostic models for retinopathy of prematurity (ROP), a potentially-blinding
EyePACS 2015 Diabetic retinopathy (DR) is a leading cause of vision loss in the world and numerous cutting-edge works have built powerful deep neural networks (DNNs) to automatically classify the DR cases via the retinal fundus images (RFIs). Deep Neural Networks Diabetic Retinopathy Retinal Fundus Images
MAPLES-DR: MESSIDOR Anatomical and Pathological Labels for Explainable Screening of Diabetic Retinopathy MAPLES-DR (MESSIDOR Anatomical and Pathological Labels for Explainable Screening of Diabetic Retinopathy) [1], contains new diagnoses for Diabetic Retinopathy (DR) and Macular Edema (ME) as well as new pixel-wise segmentation maps for 10 retinal structures related to those pathologies for 198 images of the MESSIDOR public fundus dataset [2].
The
OCT OMNIBUS The database compiled for this study is a comprehensive and meticulously curated repository designed to evaluate the efficacy of anti-VEGF therapy in patients with Diabetic Macular Edema (DME). It includes clinical and imaging data from 193 diabetic patients, aged 18-70 years, who participated in a single-center, randomized, parallelgroup, double-masked clinical trial. The database encompasses detailed demographic and clinical information, such as age, gender, medical history,
Data from: Quantifying changes on optical coherence tomography in eyes receiving treatment for neovascular age-related macular degeneration Purpose Application of artificial intelligence (AI) to macular optical
coherence tomography (OCT) scans to segment and quantify volumetric change
of anatomical and pathological features during intravitreal treatment for
neovascular age-related macular degeneration (AMD). Design Retrospective
analysis of OCT images from the Eye Hospital AMD Database.
A Composite Retinal Fundus and OCT Dataset with Detailed Clinical Markings of Retinal Layers and Retinal Lesions to Grade Macular and Glaucomatous Disorders This repository contains composite retinal fundus and OCT dataset for analyzing retinal layers, retinal lesions, and to diagnose normal and abnormal retinal diseases like Centrally Involved Diabetic Macular Edema, Acute Central Serous Retinopathy (CSR), Chronic CSR, Geographic Age-related Macular Degeneration (AMD), Neovascular AMD, and
Comparisons of choroidal nevus measurements obtained using 10- and 20-MHz ultrasound and spectral domain optical coherence tomography ABSTRACT Purpose: To compare measurements of lesions clinically diagnosed as choroidal nevi using spectral domain optical coherence tomography (SD-OCT) and 10- and 20-MHz ultrasound (US). Methods: This prospective study, which was conducted between May 2011 and December 2011, evaluated eyes diagnosed with choroidal nevus via photographic documentation using 10-
VETO: Vessel topology manual annotation dataset We have labeled the vessel topology on four public-available retinal datasets:

INSPIRE [1]VICAVR [2]IOSTAR [3]DRIVE [4]Two experts were asked to manually label the topological information of the retinal vascular structure by using a graph editing software we developed for this task. Expert one and two independently labeled each vessel segment or centerline for all datasets, based on the types of available manual annotations of the vessel
Iris image segmentation for personal identification using unit gradient vectors and Iris-SAM Iris recognition is one of a fundamental component of computer vision, extensively employed for authentication and identification within security and forensic contexts. Despite its broad utilization, the efficacy of current iris recognition systems is notably compromised under challenging conditions such as suboptimal lighting, partial iris visibility, and eye mis- alignment. These adverse circumstances
Synthetic Glaucoma Progression Dataset and Analysis Code for "ChatGPT-5–Assisted Nonlinear Time-Series Analysis of Glaucoma Progression Exact synthetic dataset (300 patients / 600 eyes; ~5-year irregular follow-up) and analysis pipeline matching the manuscript “ChatGPT-5–Assisted Nonlinear Time-Series Analysis of Glaucoma Progression: Persistence, Volatility, and Rapid Deterioration Metrics from a Fully Synthetic Cohort”.

The archive includes:

Main dataset in CSV/XLSX format (exact version
Evaluation of peripapillary choroidal and retinal nerve fiber layer thickness in eyes with tilted optic disc Purpose: This study was performed to evaluate the retinal nerve fiber layer (RNFL) and peripapillary choroidal thickness in eyes with tilted optic disc in order to identify characteristic RNFL and peripapillary choroid patterns verified by optical coherence tomography (OCT). Methods: Twenty-nine eyes of 29 patients with tilted optic discs were studied with spectral-domain (SD)-OCT and
Data_FAZ_area.xlsx This observational study was approved by the Institutional Review Board of the Kyoto University Graduate School of Medicine (Kyoto, Japan) and adhered to the tenets of the Declaration of Helsinki. Written informed consent was obtained from each participant before initiating any study procedures or examinations. We included 14 healthy eyes of 14 healthy volunteers with no history or evidence of systemic and macula diseases.
Each participant underwent high-resolution
Deep Learning for the Detection and Classification of Diabetic Retinopathy with an Improved Activation Function Diabetic retinopathy (DR) is an eye disease triggered due to diabetes, which may lead to blindness. To prevent diabetic patients from becoming blind, early diagnosis and accurate detection of DR are vital. Deep learning models, such as convolutional neural networks (CNNs), are largely used in DR detection through the classification of blood vessel pixels from the remaining pixels. In