Intraretinal Layer Segmentation of Macular Optical Coherence Tomography Images Using Optimal 3-D Graph Search

Abstract

Current techniques for segmenting macular optical coherence tomography (OCT) images have been 2-D in nature. Furthermore, commercially available OCT systems have only focused on segmenting a single layer of the retina, even though each intraretinal layer may be affected differently by disease. We report an automated approach for segmenting (anisotropic) 3-D macular OCT scans into five layers. Each macular OCT dataset consisted of six linear radial scans centered at the fovea. The six surfaces defining the five layers were identified on each 3-D composite image by transforming the segmentation task into that of finding a minimum-cost closed set in a geometric graph constructed from edge/regional information and a priori determined surface smoothness and interaction constraints. The method was applied to the macular OCT scans of 12 patients (24 3-D composite image datasets) with unilateral anterior ischemic optic neuropathy (AION). Using the average of three experts’ tracings as a reference standard resulted in an overall mean unsigned border positioning error of 6.1 ± 2.9 µm, a result comparable to the interobserver variability (6.9 ± 3.3 µm). Our quantitative analysis of the automated segmentation results from AION subject data revealed that the inner retinal layer thickness for the affected eye was 24.1 µm (21%) smaller on average than for the unaffected eye (P < 0.001), supporting the need for segmenting the layers separately.

Depth-encoded all-fiber swept source polarization sensitive OCT

“…Two coupled surfaces were simultaneously searched in 3D with a global optimum guarantee. In retinal OCT images, individual cost functions were designed for each boundary according to Garvin et al [67] such that the first surface corresponds to the internal limiting membrane (ILM), and the second surface corresponds to the retinal pigmented epithelium (RPE). Multi-resolution techniques were used to speed up the processing [68].…”

An automated framework of inner segment/outer segment defect detection for retinal SD-OCT images

“…The SD-OCT volume was automatically segmented into 10 intra-retinal layers identified in Fig.2 using the multi-scale 3-D graph-search approach [15][16][17][18], producing 11 surfaces. The basic idea of this method is to detect the retinal surface in high-resolution sub-volume, which contains the surface and has been segmented at a lower resolution.…”

A Review of Algorithms for Segmentation of Retinal Image Data Using Optical Coherence Tomography

“…The early development of Haecker et al's algorithm extracted only 2 intratretinal layers and was evaluated on data from 18 controls and 9 subjects with papilledema (Haecker et al, 2006). This approach was further developed into a multilayer segmentation (Garvin et al, 2008) showing superior results for high quality OCT data. This graph-search approach potentially increased the accuracy of segmentation by using weights describing both edge and regional information to segment the volume.…”