Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology

Pratul P Srinivasan,Sina Farsiu,Vadim Y Arshavsky,Stephanie J Heflin,Joseph A Izatt

doi:10.1364/boe.5.000348

Pratul P Srinivasan, Sina Farsiu + Show 3 more

Open Access

https://doi.org/10.1364/boe.5.000348

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Abstract

Accurate quantification of retinal layer thicknesses in mice as seen on optical coherence tomography (OCT) is crucial for the study of numerous ocular and neurological diseases. However, manual segmentation is time-consuming and subjective. Previous attempts to automate this process were limited to high-quality scans from mice with no missing layers or visible pathology. This paper presents an automatic approach for segmenting retinal layers in spectral domain OCT images using sparsity based denoising, support vector machines, graph theory, and dynamic programming (S-GTDP). Results show that this method accurately segments all present retinal layer boundaries, which can range from seven to ten, in wild-type and rhodopsin knockout mice as compared to manual segmentation and has a more accurate performance as compared to the commercial automated Diver segmentation software.

Highlights

Accurate quantification of retinal layer thicknesses in spectral domain optical coherence tomography (SD-OCT) images of mouse eyes is crucial for the study and initial treatment evaluation of many ophthalmic and neurologic diseases in humans [1, 2]
We previously developed a framework for segmenting retinal layers in human eyes based on graph theory and dynamic programming (GTDP) [4]
SD-OCT images are corrupted by speckle noise, so it is beneficial to denoise them to reduce the effect of noise on the segmentation results

Summary

Introduction

Accurate quantification of retinal layer thicknesses in spectral domain optical coherence tomography (SD-OCT) images of mouse eyes is crucial for the study and initial treatment evaluation of many ophthalmic and neurologic diseases in humans [1, 2]. Segmenting these layers manually [1, 3] is time-consuming, limiting its practicality for use in large-scale studies. While this paper was under review, a new work by Antony and colleagues was published which addressed a graph-based method for the automated segmentation of 10 retinal layer boundaries in normal mice, excluding the optic nerve head (ONH) region [17]

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