Retinal layer segmentation of macular OCT images using boundary classification

Abstract

Optical coherence tomography (OCT) has proven to be an essential imaging modality for ophthalmology and is proving to be very important in neurology. OCT enables high resolution imaging of the retina, both at the optic nerve head and the macula. Macular retinal layer thicknesses provide useful diagnostic information and have been shown to correlate well with measures of disease severity in several diseases. Since manual segmentation of these layers is time consuming and prone to bias, automatic segmentation methods are critical for full utilization of this technology. In this work, we build a random forest classifier to segment eight retinal layers in macular cube images acquired by OCT. The random forest classifier learns the boundary pixels between layers, producing an accurate probability map for each boundary, which is then processed to finalize the boundaries. Using this algorithm, we can accurately segment the entire retina contained in the macular cube to an accuracy of at least 4.3 microns for any of the nine boundaries. Experiments were carried out on both healthy and multiple sclerosis subjects, with no difference in the accuracy of our algorithm found between the groups.

Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search

“…Following the intensity normalization method in [32], we first rescale the intensity values of the B-scan X, X I , to be between [0, 1] as follows:…”

“…Machine learning based methods formulate layer segmentation as a classification problem, where features are extracted from each layer or its boundaries and used to train a classifier (e.g. support vector machine, neural networks, or random forest classifiers) for determining layer boundaries [29,[31][32][33].…”

Disease-modifying therapies modulate retinal atrophy in multiple sclerosis

“…8 An automated macular segmentation method described in detail elsewhere, 13 with slight modifications of the size of regions from which measurements were derived, was used to compute thicknesses of the GCIP, inner nuclear layer (INL), outer nuclear layer (ONL), and average macular thickness (AMT). The segmentation method uses a validated algorithm 14 that generates thickness measurements by averaging the thickness values within a 535 mm circle centered at the fovea. The foveal region consisting of a 131 mm circle is excluded from analysis.…”

Shared-hole graph search with adaptive constraints for 3D optic nerve head optical coherence tomography image segmentation

“…Chiu et al [20] proposed an automatic method for segmenting retinal layers using graph theory and dynamic programming (GTDP). Lang et al [21] built a random forest classifier to segment eight retinal layers in macular centered OCT images. Recently, deep learning [22] has been demonstrated to be a powerful tool in many fields.…”