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A comprehensive assessment of the retina OCT images is essential in clinical and experimental ophthalmology for the detection and accurate interpretation of the structural and functional abnormalities of the retina. An accurate retinal layer segmentation will allow the measurement of the local and global changes in retina. Herein, we present a computational framework based on a fast-automatic graph-cut based algorithm for an accurate segmentation of the retinal layers for multiple species including human, non-human primate (rhesus macaque) and mouse eye. The proposed algorithm allowed the simultaneous extraction of the multiple biomarkers including retinal layer thickness maps, retinal layer reflectivity maps and layer-specific vascular maps.
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Ratheesh Kumar Meleppat, Karuna K. Kothandath, Glenn C. Yiu, Robert J. Zawadzki, "Automatic extraction of multiple biomarkers of retinal diseases from OCT/OCTA images using 3D graph-cut segmentation approach," Proc. SPIE 11623, Ophthalmic Technologies XXXI, 116230M (5 March 2021); https://doi.org/10.1117/12.2578661