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Amr Elsawy, Giovanni Gregori, Taher Eleiwa, Mohamed Abdel-Mottaleb, Mohamed Abou Shousha; Pathological-Corneas Layer Segmentation and Thickness Measurement in OCT Images. Trans. Vis. Sci. Tech. 2020;9(11):24. doi: https://doi.org/10.1167/tvst.9.11.24.
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The purpose of this study was to propose a new algorithm for the segmentation and thickness measurement of pathological corneas with irregular layers using a two-stage graph search and ray tracing.
In the first stage, a graph, with only gradient edge-cost, is used to segment the air-epithelium and endothelium-aqueous boundaries. In the second stage, a graph, with gradient, directional, and multiplier edge-cost, is used to correct segmentation. The optical coherence tomography (OCT) image is flattened using the air-epithelium boundary and a graph search is used to segment the epithelium-Bowman's and Bowman's-stroma boundaries. Then, the OCT image is flattened using the endothelium-aqueous boundary and a graph search is used to segment the Descemet's membrane. Ray tracing is used to correct the inter-boundary distances, then the thickness is measured using the shortest distance. The proposed algorithm was trained and evaluated using 190 OCT images manually segmented by trained operators.
The mean and standard deviation of the unsigned errors of the algorithm-operator and inter-operator were 0.89 ± 1.03 and 0.77 ± 0.68 pixels in segmentation and 3.62 ± 3.98 and 2.95 ± 2.52 µm in thickness measurement.
Our proposed algorithm can produce accurate segmentation and thickness measurements compared with the manual operators.
Our algorithm could be potentially useful in the clinical practice.
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