AI Case Study

National University of Singapore researchers use deep learning to outperform current methods in detecting glaucoma progression in patients

Researchers from the National University of Singapore developed a new deep learning approach to detect changes in the eyes of glaucoma patients. Using tomography images, the approach is able to automatically segment and highlight six different parameters of the optic nerve head's structure. In testing the approach has shown significantly better results in the visual analysis of the optic nerve head in comparison to other deep learning methods.

Industry

Public And Social Sector

Education And Academia

Project Overview

"The researchers, from the National University of Singapore and elsewhere, created a new, customized deep learning approach with one algorithm that automatically segments and highlights six different structural parameters of the optic nerve head at the same time.

The approach, called the Dilated-Residual U-Net, or DRUNET, is inspired by U-Net, a convoluted neutral network developed for biomedical image segmentation. DRUNET is comprised of two “towers.” One is a downsampling tower to capture contextual information, such as the spatial arrangement of the tissues; the other is an upsampling tower to capture local information, like tissue texture.

The study authors recruited 100 subjects at Singapore National Eye Center and ended up with 40 healthy controls, 41 individuals with primary open-angle glaucoma, and 19 with primary closed-angle glaucoma. Researchers used manual segmentation to train the algorithm to identify and isolate optic nerve head tissues and employed some data augmentation, since they had a relatively small data set of scans. The scans were split into training and testing data sets.

In testing, overall DRUNET performed significantly better at segmenting and highlighting almost all local and contextual features of the tissues in the tomography images of the optic nerve head than other deep learning methods, researchers found. For one type of tissue, retina pigment epithelium (which lines and protects other tissues), DRUNET’s performance was similar to the other deep learning methods." (healthdatamanagement)

Reported Results

The researchers state the following results:

*better performance at "segmenting and highlighting almost all local and contextual features of the tissues in the tomography images of the optic nerve head" in comparison to other deep learning methods
* " less computationally expensive and faster because it needs fewer trainable parameters".

Technology

"In this study, we developed the architecture DRUNET (Dilated-Residual U-Net): a fully convolutional neural network inspired by the widely used U-Net [37], to segment the individual ONH tissues" (paper)

Function

R And D

Core Research And Development

Background

"Glaucoma is a group of diseases that damage the eye’s optic nerve and can cause vision loss, including blindness. Although there is no cure, early detection and treatment can delay its progression. The progression is marked by complex structural changes in the optic nerve head tissues, such as the thinning of retinal nerve fiber layers and the width of membranes.

Current deep learning methods applied to optical coherence tomography, which uses light to take cross-section images, can detect these changes automatically, but existing methods require a different tissue-specific algorithm to examine each type of tissue. This is also computationally expensive and prone to segmentation errors." (healthdatamanagement)

Benefits

Data

Tomography images of the optic nerve head