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Diabetic Retinopathy Dataset

Can Anyone Provide The Datasets/fundus Images With Ground Truth Values For The Detection Of Diabetic Retinopathy Based On Exudates?

Can Anyone Provide The Datasets/fundus Images With Ground Truth Values For The Detection Of Diabetic Retinopathy Based On Exudates?

Objective To validate a mathematical algorithm that calculates risk of diabetic retinopathy progression in a diabetic population with UK staging (R03; M1) of diabetic retinopathy. To establish the utility of the algorithm to reduce screening frequency in this cohort, while maintaining safety standards. Research design and methods The cohort of 9690 diabetic individuals in England, followed for 2 years. The algorithms calculated individual risk for development of preproliferative retinopathy (R2), active proliferative retinopathy (R3A) and diabetic maculopathy (M1) based on clinical data. Screening intervals were determined such that the increase in risk of developing certain stages of retinopathy between screenings was the same for all patients and identical to mean risk in fixed annual screening. Receiver operating characteristic curves were drawn and area under the curve calculated to estimate the prediction capability. Results The algorithm predicts the occurrence of the given diabetic retinopathy stages with area under the curve =80% for patients with type II diabetes (CI 0.78 to 0.81). Of the cohort 64% is at less than 5% risk of progression to R2, R3A or M1 within 2 years. By applying a 2 year ceiling to the screening interval, patients with type II diabetes are screened on average every 20 months, which is a 40% reduction in frequency compared with annual screening. Conclusions The algorithm reliably identifies patients at high risk of developing advanced stages of diabetic retinopathy, including preproliferative R2, active proliferative R3A and maculopathy M1. Majority of patients have less than 5% risk of progression between stages within a year and a small high-risk group is identified. Screening visit frequency and presumably costs in a diabetic retinopathy Continue reading >>

Detecting Diabetic Retinopathy In Eye Images

Detecting Diabetic Retinopathy In Eye Images

The past almost four months I have been competing in a Kaggle competition about diabetic retinopathy grading based on high-resolution eye images. In this post I try to reconstruct my progression through the competition; the challenges I had, the things I tried, what worked and what didn’t. This is not meant as a complete documentation but, nevertheless, some more concrete examples can be found at the end and certainly in the code. In the end I finished fifth of the almost 700 competing teams. Update 02/08/2015: Code and models (with parameters) added. Introduction Introduction Diabetic retinopathy (DR) is the leading cause of blindness in the working-age population of the developed world and is estimated to affect over 93 million people. (From the competition description where some more background information can be found.) The grading process consists of recognising very fine details, such as microaneurysms, to some bigger features, such as exudates, and sometimes their position relative to each other on images of the eye. (This is not an exhaustive list, you can look at, for example, the long list of criteria used in the UK to grade DR (pdf).) Some annotated examples from the literature to get an idea of what this really looks like (the medical details/terminology are not very important for the rest of this post): Example of non-proliferative diabetic retinopathy (NPDR): Thin arrows: hard exudates; Thick arrow: blot intra-retinal hemorrhage; Triangle: microaneurysm. (Click on image for source.) Now let’s look at it as someone who simply wants to try to model this problem. You have 35126 images in the training set that look like this annotated by a patient id and “left” or “right” (each patient has two images, one per eye) and divided into 5 fairly unbalanc Continue reading >>

Diaretdb1 - Standard Diabetic Retinopathy Database

Diaretdb1 - Standard Diabetic Retinopathy Database

Copyright IMAGERET project. All rights reserved. Last modified: $Date: 2007/06/19 10:03:24 $ DIARETDB1 - Standard Diabetic Retinopathy Database This is a public database for benchmarking diabetic retinopathydetection from digital images. The main objective of the designhas been to unambiguously define a database and atesting protocol which can be used to benchmark diabetic retinopathydetection methods. By using this database and the defined testingprotocol, the results between different methods can be compared. Formore information refer to the documentation. The database can be freely downloaded and used for scientific research purposes. You are not allowed redistribute or modify it. Proper citing of this resource is expected if the database is used in research or other reporting. The database is meant to be useful, but it is distributed WITHOUT ANY WARRANTY, and without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. Inclusion of this database or even parts of it in a proprietary program is not allowed without a written permission from the owners of the copyright. If you wish to obtain such a permission, you should contact Machine Vision and Pattern Recognition Research Group Laboratory of Information Processing Lappeenranta University of Technology PO Box 20 FI-53851 Lappeenranta FINLAND or owner-diaretdblists.lut.fi (in the address, replace the string with @, please). All communication related to the database updates and announcements of future releases will be carried out through the diaretdb mailing list. You can subscribe the list by sending a message to the address diaretdb-onlists.lut.fi by using your default e-mail system. You will get a confirmation with further instructions after your subscription. If you find any errors or Continue reading >>

High-resolution Fundus (hrf) Image Database

High-resolution Fundus (hrf) Image Database

High-Resolution Fundus (HRF) Image Database This database has been established by a collaborative research group to support comparative studies on automatic segmentation algorithms on retinal fundus images. The database will be iteratively extended and the webpage will be improved. We would like to help researchers in the evaluation of segmentation algorithms. We encourage anyone working with segmentation algorithms who found our database useful to send us their evaluation results with a reference to a paper where it is described. This way we can extend our database of algorithms with the given results to keep it always up-to-date. The database can be used freely for research purpuses. If you are using our database to evaluate your methods, please cite Budai, Attila; Bock, Rdiger; Maier, Andreas; Hornegger, Joachim; Michelson, Georg. Robust Vessel Segmentation in Fundus Images . International Journal of Biomedical Imaging, vol. 2013, 2013 The public database contains at the moment 15 images of healthy patients, 15 images of patients with diabetic retinopathy and 15 images of glaucomatous patients. Binary gold standard vessel segmentation images are available for each image. Also the masks determining field of view (FOV) are provided for particular datasets. The gold standard data is generated by a group of experts working in the field of retinal image analysis and clinicians from the cooperated ophthalmology clinics. We intend to add further gold standard data to the existing images to help the evaluation of algorithms which localize the macula, optic disc, or differentiate between arteries and veins. Earlier stages of the datasets are available here We captured 18 image pairs of the same eye from 18 human subjects using a Canon CR-1 fundus camera with a field of view Continue reading >>

Github - Javathunderman/retinopathy-dataset: A Curated Version Of The Dataset Used While Developing The Diabetic-retinopathy-screening Project.

Github - Javathunderman/retinopathy-dataset: A Curated Version Of The Dataset Used While Developing The Diabetic-retinopathy-screening Project.

A curated version of the dataset used while developing the diabetic-retinopathy-screening project. If nothing happens, download GitHub Desktop and try again. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. A curated version of the dataset used while developing the diabetic-retinopathy-screening project. This repository does not contain all of the images used while creating the diabetic-retinopathy-screening project. As such, please note that the predetermined file lists in the "logs/" directory of the project may include files that are not available with this dataset. The full dataset is hosted here: . Only the "train_00x.zip" files were used. In order to use the full dataset, all of the training zip files must be downloaded and extracted together. Main repository here: Continue reading >>

R: Diabetic Retinopathy

R: Diabetic Retinopathy

A trial of laser coagulation as a treatment to delaydiabetic retinopathy. A data frame with 394 observations on the following 9 variables. type of diabetes: juvenile adult,(diagnosis before age 20) 0 = censored, 1 = loss of vision in this eye a risk score for the eye. This high risksubset is defined as a score of 6 or greater in at least one eye. The 197 patients in this dataset were a 50% random sample of thepatients with "high-risk" diabetic retinopathy as defined by theDiabetic Retinopathy Study (DRS). Each patient had one eye randomizedto laser treatment and the other eye received no treatment,and has two observations in the data set.For eacheye, the event of interest was the time from initiation of treatmentto the time when visual acuity dropped below 5/200 two visits in a row.Thus there is a built-in lag time ofapproximately 6 months (visits were every 3 months). Survival timesin this dataset are the actual time to vision loss in months,minus the minimum possible time to event (6.5 months). Censoring wascaused by death, dropout, or end of the study. W. J. Huster, R. Brookmeyer and S. G. Self (1989).Modelling paired survival data with covariates,Biometrics 45:145-156. A. L. Blair, D. R. Hadden, J. A. Weaver, D. B. Archer, P. B. Johnstonand C. J. Maguire (1976). The 5-year prognosis for vision in diabetes,American Journal of Ophthalmology, 81:383-396. coxph(Surv(futime, status) ~ type + trt + cluster(id), retinopathy) Continue reading >>

Retinopathy Online Challenge

Retinopathy Online Challenge

Welcome to the Retinopathy Online Challenge (ROC) website. We have experienced some major problems with our database server and can only provide you with the image files by download.View data, data annotation, registration, and submission are no longer possible. Datasets may be downloaded using the links below.) The ROC aims to help patients with diabetes through improving computer aided detection and diagnosis (CAD) of diabetic retinopathy . Diabetic retinopathy is the second largest cause of blindness in the US and Europe. Most visual loss and blindness from diabetic retinopathy can be prevented through early diagnosis or screening. Computer algorithms have been developed in order to detect the signs of diabetic retinopathy from retinal images obtained using a digital retinal camera. However, few, if any of these algorithms have been applied in clinical practice. ROC facilitates the translation of diabetic retinopathy CAD into clinical practice by: enabling any medical image analysis research group to develop diabetic retinopathy CAD algorithms by offering a training set of retinal images with reference standard provided by internationally accepted retinal experts. evaluating the output of a diabetic retinopathy CAD algorithm in a uniform manner on a supplied test set, allowing algorithms to be compared both to other algorithms and retinal experts. organizing meetings and workshops at international conferences to compare CAD systems, following the paradigm of revolution through competition. Currently, we have released a first data set, aimed at CAD of microaneurysms and dot hemorrhages. These abnormalities are amongst the first signs of the presence of diabetic retinopathy. For some example lesions see Figure 1 below. On this site, interested research groups and comp Continue reading >>

Bigdata Analytics On Diabetic Retinopathy Study (drs) On Real-time Data Set Identifying Survival Time And Length Of Stay

Bigdata Analytics On Diabetic Retinopathy Study (drs) On Real-time Data Set Identifying Survival Time And Length Of Stay

In this paper, we had analyzed a large scale Diabetic data sets for several patients to find the length of time taken for treatment for each class of Diabetes and the risk of re-admission of diabetic patients performing Bigdata analytics, the type of diabetes and its outcome which acted as a high risk sample of patient data sets. We have collected and integrated different sources of diabetic information for several patients, from primary and secondary treatment information to administrative information, to analyze novel view of patient care processes such as type of treatments and every patient behaviors on which results multifaceted nature of chronic care that we take into our account to predict the survival factors and length of stay. Nowadays by using electronic medical equipments with high quality and high degree calibrations, we are able to gather large amounts of real-time diabetic data sets. The requires the usage of distributed platforms for making BigData analysis that results on making decisions based on available data and its trends. This type of Bigdata analysis allows geographical and environmental information of patients enables the capability of interpreting the ethnicity of data gathered and extract new analysis to identify survival options and treatment timelines (LOS) from them. Continue reading >>

Uci Machine Learning Repository: Diabetic Retinopathy Debrecen Data Set Data Set

Uci Machine Learning Repository: Diabetic Retinopathy Debrecen Data Set Data Set

Diabetic Retinopathy Debrecen Data Set Data Set Abstract: This dataset contains features extracted from the Messidor image set to predict whether an image contains signs of diabetic retinopathy or not. 1. Dr. Balint Antal, Department of Computer Graphics and Image Processing Faculty of Informatics, University of Debrecen, 4010, Debrecen, POB 12, Hungary 2. Dr. Andras Hajdu, Department of Computer Graphics and Image Processing Faculty of Informatics, University of Debrecen, 4010, Debrecen, POB 12, Hungary This dataset contains features extracted from the Messidor image set to predict whether an image contains signs of diabetic retinopathy or not. All features represent either a detected lesion, a descriptive feature of a anatomical part or an image-level descriptor. The underlying method image analysis and feature extraction as well as our classification technique is described in Balint Antal, Andras Hajdu: An ensemble-based system for automatic screening of diabetic retinopathy, Knowledge-Based Systems 60 (April 2014), 20-27. The image set (Messidor) is available at [Web Link] . 0) The binary result of quality assessment. 0 = bad quality 1 = sufficient quality. 1) The binary result of pre-screening, where 1 indicates severe retinal abnormality and 0 its lack. 2-7) The results of MA detection. Each feature value stand for the number of MAs found at the confidence levels alpha = 0.5, . . . , 1, respectively. 8-15) contain the same information as 2-7) for exudates. However, as exudates are represented by a set of points rather than the number of pixels constructing the lesions, these features are normalized by dividing the number of lesions with the diameter of the ROI to compensate different image 16) The euclidean distance of the center of the macula and the center of t Continue reading >>

Improved Automated Detection Of Diabetic Retinopathy On A Publicly Availabledataset Through Integration Of Deep Learning.

Improved Automated Detection Of Diabetic Retinopathy On A Publicly Availabledataset Through Integration Of Deep Learning.

1. Invest Ophthalmol Vis Sci. 2016 Oct 1;57(13):5200-5206. doi:10.1167/iovs.16-19964. Improved Automated Detection of Diabetic Retinopathy on a Publicly AvailableDataset Through Integration of Deep Learning. Abrmoff MD(1), Lou Y(2), Erginay A(3), Clarida W(4), Amelon R(4), Folk JC(5),Niemeijer M(4). (1)Department of Ophthalmology and Visual Sciences, University of Iowa Hospitals and Clinics, Iowa City, Iowa, United States 2Iowa City Veterans Affairs Medical Center, Iowa City, Iowa, United States 3IDx LLC, Iowa City, Iowa, United States. (2)Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, Iowa, United States. (3)Service d' Ophtalmologie, Hpital Lariboisire, APHP, Paris, France. (4)IDx LLC, Iowa City, Iowa, United States. (5)Department of Ophthalmology and Visual Sciences, University of Iowa Hospitals and Clinics, Iowa City, Iowa, United States 3IDx LLC, Iowa City, Iowa, United States. Purpose: To compare performance of a deep-learning enhanced algorithm forautomated detection of diabetic retinopathy (DR), to the previously publishedperformance of that algorithm, the Iowa Detection Program (IDP)-without deeplearning components-on the same publicly available set of fundus images andpreviously reported consensus reference standard set, by three US Board certifiedretinal specialists.Methods: We used the previously reported consensus reference standard ofreferable DR (rDR), defined as International Clinical Classification of Diabetic Retinopathy moderate, severe nonproliferative (NPDR), proliferative DR, and/ormacular edema (ME). Neither Messidor-2 images, nor the three retinal specialists setting the Messidor-2 reference standard were used for training IDx-DR versionX2.1. Sensitivity, specificity, negative predictive value, area under th Continue reading >>

Idrid - Home

Idrid - Home

The aim of this challenge is to evaluate algorithms for automated detection and grading of diabetic retinopathy and diabetic macular edema using retinal fundus images. Diabetic Retinopathy is the most prevalent cause of avoidable vision impairment, mainly affecting working age population in the world. Recent research has given a better understanding of requirement in clinical eye care practice to identify better and cheaper ways of identification, management, diagnosis and treatment of retinal disease. The importance of diabetic retinopathy screening programs and difficulty in achieving reliable early diagnosis of diabetic retinopathy at a reasonable cost needs attention to develop computer-aided diagnosis tool. Computer-aided disease diagnosis in retinal image analysis could ease mass screening of population with diabetes mellitus and help clinicians in utilizing their time more efficiently. The recent technological advances in computing power, communication systems, and machine learning techniques provide opportunities to the biomedical engineers and computer scientists to meet the requirements of clinical practice. Diverse and representative retinal image sets are essential for developing and testing digital screening programs and the automated algorithms at their core. To the best of our knowledge, the database for this challenge, IDRiD (Indian Diabetic Retinopathy Image Dataset), is the first database representative of an Indian population. Moreover, it is the only dataset constituting typical diabetic retinopathy lesions and normal retinal structures annotated at a pixel level. This dataset provides information on the disease severity of diabetic retinopathy, and diabetic macular edema for each image. This makes it perfect for development and evaluation of image Continue reading >>

Google Achieves Healthcare Breakthrough Using Eyepacs Retinal Images

Google Achieves Healthcare Breakthrough Using Eyepacs Retinal Images

Question: Can a computer using deep learning (a new type of artificial intelligence) be successfully applied to medical imaging? In other words, can a computer, given enough practice examples, learn to detect diabetic retinal disease as well as a board-certified medical specialist? More specifically, is it possible for a computer to create its own algorithm that will allow it to examine images of human retinas and correctly diagnose diabetic retinopathy (DR) or macular edema? Answer: Google has just announced that an algorithm based on deep learning had high sensitivity and specificity for detecting referable diabetic retinopathy. The study was published in the Journal of the American Medical Association (JAMA) on December 1, 2016. So, why is this announcement so important to primary care? Because, according to Googles announcement, automated grading of diabetic retinopathy has potential benefits such as increasing efficiency and coverage of screening programs; reducing barriers to access; and improving patient outcomes by providing early detection. Those three benefits resonate loudly in the healthcare safety net, where access to care and screening is a challenge, and achieving better patient outcomes in chronic disease management is always high on the list of priorities in any primary care setting. The study was led by Lily Peng, MD, PhD, of Google Research, Inc., using retinal images provided by EyePACS as well as sources in France and India. EyePACS (which stands for Eye Picture Archive Communication System) places digital cameras in primary care clinics to image the retinas of diabetic patients and then upload the images to the cloud where they are read by certified specialists who render an opinion and recommendation within 24 hours. Thousands of competing algori Continue reading >>

Laser Marks Dataset

Laser Marks Dataset

Photocoagulation is the most common and accepted treatment of Proliferative Retinopathy and Diabetic Macular Edema, both advanced stages of Diabetic Retinopathy (DR). This type of treatment leaves scars on the retina derived from the laser incidence. We will call these scars laser marks. Currently, DR screening programs rely on automatic diagnostic algorithms that detect DR-related lesions. Laser marks may deteriorate the performance of these systems, hence it is important to detect them. The Laser Mark Dataset (LMD) is a database that enables comparative studies on segmentation of laser marks in retinal images. The research community is invited to test their laser mark detection algorithms on this database. The LMD is divided in two different groups of images: one where the images were acquired during a DR program (Diabetic Retinopathy Screening) and the other one where the images were captured in a clinical environment (Before and After Photocoagulation Treatment). This page provides the download links of both datasets containing retinal images with and without laser marks (Figure 1). Figure 1: Retinal image with laser marks (left) and retinal image without laser marks (right). Laser Marks Dataset - Diabetic Retinopathy Screening (LMD-DRS) The LMD-DRS contains 203 retinal images manually classified by experts as having laser marks from an ongoing DR screening program in Portugal. All of the images are non-mydriatic and have a 45 degree FOV and they were acquired in 2014. There are 26 images captured using Nidek AFC-330 Retinal Camera and with a resolution of 1920 by 1920 pixels. The remaining 177 images were captured using Canon CR6-45NM Retinal Camera and have a resolution of 768 by 584 pixels. It also includes 419 retinal images without laser marks from the same DR Continue reading >>

Diabetic Retinal Fundus Images: Preprocessing And Feature Extraction For Early Detection Of Diabetic Retinopathy | Biomedical And Pharmacology Journal

Diabetic Retinal Fundus Images: Preprocessing And Feature Extraction For Early Detection Of Diabetic Retinopathy | Biomedical And Pharmacology Journal

Diabetic Retinal Fundus Images: Preprocessing and Feature Extraction for Early Detection of Diabetic Retinopathy Dilip Singh Sisodia, Shruti Nair and Pooja Khobragade National Institute of Technology, Raipur. Corresponding Author E-mail: [email protected] DOI : The investigation of clinical reports suggested that more than ten percent patients with diabetes have a high risk of eye issues. Diabetic Retinopathy (DR) is an eye ailment which influences eighty to eighty-five percent of the patients who have diabetes for more than ten years. The retinal fundus images are commonly used for detection and analysis of diabetic retinopathy disease in clinics. The raw retinal fundus images are very hard to process by machine learning algorithms. In this paper, pre-processing of raw retinal fundus images are performed using extraction of green channel, histogram equalization, image enhancement and resizing techniques. Fourteen features are also extracted from pre-processed images for quantitative analysis. The experiments are performed using Kaggle Diabetic Retinopathy dataset, and the results are evaluated by considering the mean value and standard deviation for extracted features. The result yielded exudate area as the best-ranked feature with a mean difference of 1029.7. The result attributed due to its complete absence in normal diabetic images and its simultaneous presence in the three classes of diabetic retinopathy images namely mild, normal and severe. Diabetic retinopathy; image processing; retinal fundus images; feature extraction exudate area Sisodia D. S, Nair S, Khobragade P. Diabetic Retinal Fundus Images: Preprocessing and Feature Extraction for Early Detection of Diabetic Retinopathy. Biomed Pharmacol J 2017;10(2). Sisodia D. S, Nair S, Khobragade P. Diabetic Continue reading >>

Convolutional Bag Of Words For Diabetic Retinopathy Detection From Eye Fundus Images

Convolutional Bag Of Words For Diabetic Retinopathy Detection From Eye Fundus Images

Convolutional bag of words for diabetic retinopathy detection from eye fundus images This paper describes a methodology for diabetic retinopathy detection from eye fundus images using a generalization of the bag-of-visual-words (BoVW) method. We formulate the BoVW as two neural networks that can be trained jointly. Unlike the BoVW, our model is able to learn how to perform feature extraction, feature encoding, and classification guided by the classification error. The model achieves 0.97 area under the curve (AUC) on the DR2 dataset while the standard BoVW approach achieves 0.94 AUC. Also, it performs at the same level of the state-of-the-art on the Messidor dataset with 0.90 AUC. Diabetic retinopathy (DR) is a complication of diabetes mellitus, wherein micro aneurysms start to form in the tiny vessels of the retina. In later stages of the disease, some retinal blood vessels may become blocked causing vision loss. Patients often do not have symptoms of the disease in its early stages which makes early diagnosis hard. DR is the leading cause of blindness and visual loss in the working age population and the second most common cause in the USA [ 1 ]. Early detection of diabetic retinopathy is paramount for the success of the treatment, as it can prevent up to 98% of severe vision loss [ 2 ]. One way of performing the diagnosis of DR is by visually inspecting eye fundus images in order to detect retinal lesions. Examples of eye fundus images taken from the Messidor [ 3 ] dataset can be seen in Fig. 1 . Although there are several grades of DR, we are only interested in the task of detecting the disease. Examples of eye fundus images of an healthy retina (a) and a retina with diabetic retinopathy (b). a Normal retina. b Pathological retina This work poses the task of discri Continue reading >>

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