Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images

TL;DR

This paper presents a deep learning approach for accurate segmentation and quantification of anterior segment structures in OCT images, aiding glaucoma diagnosis with high precision and automated quality checks.

Contribution

A novel deep convolutional neural network was developed for precise localization and segmentation of eye structures in OCT images, even with limited training data.

Findings

Achieved Dice coefficient of 95.7% for segmentation

Detected scleral spur as accurately as ophthalmologists

Enabled automated extraction of clinically relevant parameters

Abstract

Accurate isolation and quantification of intraocular dimensions in the anterior segment (AS) of the eye using optical coherence tomography (OCT) images is important in the diagnosis and treatment of many eye diseases, especially angle closure glaucoma. In this study, we developed a deep convolutional neural network (DCNN) for the localization of the scleral spur, and the segmentation of anterior segment structures (iris, corneo-sclera shell, anterior chamber). With limited training data, the DCNN was able to detect the scleral spur on unseen ASOCT images as accurately as an experienced ophthalmologist; and simultaneously isolated the anterior segment structures with a Dice coefficient of 95.7%. We then automatically extracted eight clinically relevant ASOCT parameters and proposed an automated quality check process that asserts the reliability of these parameters. When combined with an OCT machine capable of imaging multiple radial sections, the algorithms can provide a more complete objective assessment. This is an essential step toward providing a robust automated framework for reliable quantification of ASOCT scans, for applications in the diagnosis and management of angle closure glaucoma.