Deep semantic segmentation of Diabetic Retinopathy lesions: what metrics really tell us

2020 ◽  
Author(s):  
Pedro Furtado
Keyword(s):  
Diabetic Retinopathy ◽  
Semantic Segmentation

Evaluation of a New Neural Network Classifier for Diabetic Retinopathy

2021 ◽  
pp. 193229682110426
Author(s):  
Or Katz ◽  
Dan Presil ◽  
Liz Cohen ◽  
Roi Nachmani ◽  
Naomi Kirshner ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Screening Program ◽  
Semantic Segmentation ◽  
Medical Image Segmentation ◽  
Grading System ◽  
Sufficient Information ◽  
Training Procedure ◽  
Screening Process ◽  
Diabetic Eye Screening

Background: Medical image segmentation is a well-studied subject within the field of image processing. The goal of this research is to create an AI retinal screening grading system that is both accurate and fast. We introduce a new segmentation network which achieves state-of-the-art results on semantic segmentation of color fundus photographs. By applying the net-work to identify anatomical markers of diabetic retinopathy (DR) and diabetic macular edema (DME), we collect sufficient information to classify patients by grades R0 and R1 or above, M0 and M1. Methods: The AI grading system was trained on screening data to evaluate the presence of DR and DME. The core algorithm of the system is a deep learning network that segments relevant anatomical features in a retinal image. Patients were graded according to the standard NHS Diabetic Eye Screening Program feature-based grading protocol. Results: The algorithm performance was evaluated with a series of 6,981 patient retinal images from routine diabetic eye screenings. It correctly predicted 98.9% of retinopathy events and 95.5% of maculopathy events. Non-disease events prediction rate was 68.6% for retinopathy and 81.2% for maculopathy. Conclusion: This novel deep learning model was trained and tested on patient data from annual diabetic retinopathy screenings can classify with high accuracy the DR and DME status of a person with diabetes. The system can be easily reconfigured according to any grading protocol, without running a long AI training procedure. The incorporation of the AI grading system can increase the graders’ productivity and improve the final outcome accuracy of the screening process.

scholarly journals Semantic Segmentation of Eye Fundus Images Using Convolutional Neural Networks

2019 ◽  
Vol 85 ◽  
pp. 135-147
Author(s):  
Ričardas Toliušis ◽  
Olga Kurasova ◽  
Jolita Bernatavičienė
Keyword(s):  
Neural Networks ◽  
Diabetic Retinopathy ◽  
Macular Degeneration ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Optical Disk ◽  
Fundus Images ◽  
Excellent Performance ◽  
Segmentation Algorithms ◽  
Performance Results

This article reviews the problems of eye bottom fundus analysis and semantic segmentation algorithms used to distinguish the eye vessels and the optical disk. Various diseases, such as glaucoma, hypertension, diabetic retinopathy, macular degeneration, etc., can be diagnosed through changes and anomalies of the vesssels and optical disk. Convolutional neural networks, especially the U-Net architecture, are well-suited for semantic segmentation. A number of U-Net modifications have been recently developed that deliver excellent performance results.

scholarly journals Semantic Segmentation of Eye Fundus Images Using Convolutional Neural Networks

2020 ◽  
Vol 90 ◽  
pp. 116-128
Author(s):  
Ričardas Toliušis ◽  
Olga Kurasova ◽  
Jolita Bernatavičienė
Keyword(s):  
Neural Networks ◽  
Diabetic Retinopathy ◽  
Macular Degeneration ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Optical Disk ◽  
Fundus Images ◽  
Excellent Performance ◽  
Segmentation Algorithms ◽  
Performance Results

The article reviews the problems of eye bottom fundus analysis and semantic segmentation algorithms used to distinguish eye vessels, optical disk. Various diseases, such as glaucoma, hypertension, diabetic retinopathy, macular degeneration, etc., can be diagnosed by changes and anomalies of vesssels and optical disk. For semantic segmentation convolutional neural networks, especially U-Net architecture, are well suited. Recently a number of U-Net modifications have been developed that deliver excellent performance results.

Modified U-Net architecture for semantic segmentation of diabetic retinopathy images

2020 ◽  
Vol 40 (3) ◽  
pp. 1094-1109
Author(s):  
Nitigya Sambyal ◽  
Poonam Saini ◽  
Rupali Syal ◽  
Varun Gupta
Keyword(s):  
Diabetic Retinopathy ◽  
Semantic Segmentation

Apnea May Ramp Up Diabetic Retinopathy Risk

2011 ◽  
Vol 44 (13) ◽  
pp. 59
Author(s):  
SHERRY BOSCHERT
Keyword(s):  
Diabetic Retinopathy

Abstract # P-13: Characteristics of Korean T2DM Patients with Diabetic Retinopathy and Macular Edema: A Study Based on a Standardized Clinical Data

2016 ◽  
Vol 22 ◽  
pp. 19-20
Author(s):  
Sang Youl Rhee ◽  
Sejeong Park ◽  
Ki Young Kim ◽  
Suk Chon ◽  
Seung-Young Yu ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Macular Edema ◽  
Clinical Data
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