Automated 3D segmentation of intraretinal layers from optic nerve head optical coherence tomography images

Optical coherence tomography (OCT), being a noninvasive imaging modality, has begun to find vast use in the diagnosis and management of ocular diseases such as glaucoma, where the retinal nerve fiber layer (RNFL) has been known to thin. Furthermore, the recent availability of the considerably larger volumetric data with spectral-domain OCT has increased the need for new processing techniques. In this paper, we present an automated 3-D graph-theoretic approach for the segmentation of 7 surfaces (6 layers) of the retina from 3-D spectral-domain OCT images centered on the optic nerve head (ONH). The multiple surfaces are detected simultaneously through the computation of a minimum-cost closed set in a vertex-weighted graph constructed using edge/regional information, and subject to a priori determined varying surface interaction and smoothness constraints. The method also addresses the challenges posed by presence of the large blood vessels and the optic disc. The algorithm was compared to the average manual tracings of two observers on a total of 15 volumetric scans, and the border positioning error was found to be 7.25 ± 1.08 μm and 8.94 ± 3.76 μm for the normal and glaucomatous eyes, respectively. The RNFL thickness was also computed for 26 normal and 70 glaucomatous scans where the glaucomatous eyes showed a significant thinning (p < 0.01, mean thickness 73.7 ± 32.7 μm in normal eyes versus 60.4 ± 25.2 μm in glaucomatous eyes).