Dr. Vinay A. Shah Profile

Dr. Vinay A. Shah

Post Doctoral Associate at Univ of Missouri Kansas City

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Proceedings Article | 2 March 2007 Paper

Automated segmentation of intraretinal layers from macular optical coherence tomography images

Mona Haeker, Milan Sonka, Randy Kardon, Vinay Shah, Xiaodong Wu, Michael Abràmoff

Proceedings Volume 6512, 651214 (2007) https://doi.org/10.1117/12.710231

KEYWORDS: Image segmentation, Composites, Optical coherence tomography, 3D image processing, Eye, Retina, Visualization, Ophthalmology, Computing systems, 3D visualizations

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Commercially-available optical coherence tomography (OCT) systems (e.g., Stratus OCT-3) only segment and provide thickness measurements for the total retina on scans of the macula. Since each intraretinal layer may be affected differently by disease, it is desirable to quantify the properties of each layer separately. Thus, we have developed an automated segmentation approach for the separation of the retina on (anisotropic) 3-D macular OCT scans into five layers. Each macular series consisted of six linear radial scans centered at the fovea. Repeated series (up to six, when available) were acquired for each eye and were first registered and averaged together, resulting in a composite image for each angular location. The six surfaces defining the five layers were then found on each 3-D composite image series 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 with unilateral anterior ischemic optic neuropathy (corresponding to 24 3-D composite image series). The boundaries were independently defined by two human experts on one raw scan of each eye. Using the average of the experts' tracings as a reference standard resulted in an overall mean unsigned border positioning error of 6.7 ± 4.0 &mgr;m, with five of the six surfaces showing significantly lower mean errors than those computed between the two observers (p < 0.05, pixel size of 50 × 2 &mgr;m).

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