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

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.

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Semantic Segmentation of Eye Fundus Images Using Convolutional Neural Networks

Informacijos mokslai ◽

10.15388/im.2019.85.20 ◽

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.

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Semantic Segmentation of Eye Fundus Images Using Convolutional Neural Networks

Informacijos mokslai ◽

10.15388/im.2020.90.53 ◽

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.

Download Full-text