The internal limiting membrane (ILM) separates the retina and optic nerve head (ONH) from the vitreous. In the optical coherence tomography volumes of glaucoma patients, while current approaches for the segmentation of the ILM in the peripapillary and macular regions are considered robust, current approaches commonly produce ILM segmentation errors at the ONH due to the presence of blood vessels and/or characteristic glaucomatous deep cupping. Because a precise segmentation of the ILM surface at the ONH is required for computing several newer structural measurements including Bruch's membrane opening-minimum rim width (BMO-MRW) and cup volume, in this study, we propose a multimodal multiresolution graph-based method to precisely segment the ILM surface within ONH-centered spectral-domain optical coherence tomography (SD-OCT) volumes. In particular, the gradient vector flow (GVF) field, which is computed from a multiresolution initial segmentation, is employed for calculating a set of non-overlapping GVF-based columns perpendicular to the initial segmentation. The GVF columns are utilized to resample the volume and also serve as the columns to the graph construction. The ILM surface in the resampled volume is fairly smooth and does not contain the steep slopes. This prior shape knowledge along with the blood vessel information, obtained from registered fundus photographs, are incorporated in a graph-theoretic approach in order to identify the location of the ILM surface. The proposed method is tested on the SD-OCT volumes of 44 subjects with various stages of glaucoma and significantly smaller segmentation errors were obtained than that of current approaches.

Anterior segment optical coherence tomography (AS-OCT) is a non-invasive imaging modality that allows for the quantitative assessment of corneal thicknesses. Automated approaches for these measurements are not readily available and therefore measurements are often obtained manually. While graph-based approaches that enable the optimal simultaneous segmentation of multiple 3D surfaces have been proposed and applied to 3D optical coherence tomography volumes of the back of the eye, such approaches have not been applied for the segmentation of the corneal surfaces. In this work we propose adapting this graph-based method for the automated 3D segmentation of three corneal surfaces in AS-OCT images and to measure total corneal thickness. The approach is evaluated based on 34 AS-OCT volumes obtained from both eyes of 17 mice with varying corneal thicknesses. The segmentation accuracy was assessed using unsigned border positioning errors and was found to be 1.82 ± 0.81 µm. We also assessed an average relative error in total layer thickness measurements which was found to be 2.27%.

Abstract. In this work, we present a multimodal multiresolution graphbased method to segment the top surface of the retina called the internal limiting membrane (ILM) within optic-nerve-head-centered spectraldomain optical coherence tomography (SD-OCT) volumes. Having a precise ILM surface is crucial as this surface is utilized for measuring several structural parameters such as Bruch's membrane opening-minimum rim width (BMO-MRW) and cup volume. The proposed method addresses the common current segmentation errors due to the presence of retinal blood vessels, deep cupping, or a very steep slope of the ILM. In order to resolve these issues, the volume is resampled using a set of gradient vector flow (GVF) based columns. The GVF field is computed according to an initial surface segmentation which is obtained through a multiresolution framework. The retinal blood vessel information (obtained from corresponding registered fundus photographs) along with shape prior information are incorporated in a graph-theoretic approach to compute the ILM segmentation. The method is tested on the SD-OCT volumes from 44 glaucoma subjects and significantly smaller errors were obtained than that from current approaches.