Automated intraretinal layer segmentation of optical coherence tomography images using graph-theoretical methods

Priyanka Roy; Peyman Gholami; Mohana Kuppuswamy Parthasarathy; John Zelek; Vasudevan Lakshminarayanan

doi:10.1117/12.2282949

14 February 2018 Automated intraretinal layer segmentation of optical coherence tomography images using graph-theoretical methods

Priyanka Roy, Peyman Gholami, Mohana Kuppuswamy Parthasarathy, John Zelek, Vasudevan Lakshminarayanan

Author Affiliations +

Proceedings Volume 10483, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXII; 104832U (2018) https://doi.org/10.1117/12.2282949
Event: SPIE BiOS, 2018, San Francisco, California, United States

Abstract

Segmentation of spectral-domain Optical Coherence Tomography (SD-OCT) images facilitates visualization and quantification of sub-retinal layers for diagnosis of retinal pathologies. However, manual segmentation is subjective, expertise dependent, and time-consuming, which limits applicability of SD-OCT. Efforts are therefore being made to implement active-contours, artificial intelligence, and graph-search to automatically segment retinal layers with accuracy comparable to that of manual segmentation, to ease clinical decision-making. Although, low optical contrast, heavy speckle noise, and pathologies pose challenges to automated segmentation. Graph-based image segmentation approach stands out from the rest because of its ability to minimize the cost function while maximising the flow. This study has developed and implemented a shortest-path based graph-search algorithm for automated intraretinal layer segmentation of SD-OCT images. The algorithm estimates the minimal-weight path between two graph-nodes based on their gradients. Boundary position indices (BPI) are computed from the transition between pixel intensities. The mean difference between BPIs of two consecutive layers quantify individual layer thicknesses, which shows statistically insignificant differences when compared to a previous study [for overall retina: p = 0.17, for individual layers: p > 0.05 (except one layer: p = 0.04)]. These results substantiate the accurate delineation of seven intraretinal boundaries in SD-OCT images by this algorithm, with a mean computation time of 0.93 seconds (64-bit Windows10, core i5, 8GB RAM). Besides being self-reliant for denoising, the algorithm is further computationally optimized to restrict segmentation within the user defined region-of-interest. The efficiency and reliability of this algorithm, even in noisy image conditions, makes it clinically applicable.

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Priyanka Roy, Peyman Gholami, Mohana Kuppuswamy Parthasarathy, John Zelek, and Vasudevan Lakshminarayanan "Automated intraretinal layer segmentation of optical coherence tomography images using graph-theoretical methods", Proc. SPIE 10483, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXII, 104832U (14 February 2018); https://doi.org/10.1117/12.2282949

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