Presentation
5 March 2021 Automatic extraction of multiple biomarkers of retinal diseases from OCT/OCTA images using 3D graph-cut segmentation approach
Author Affiliations +
Abstract
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.
Conference Presentation
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ratheesh Kumar Meleppat, Karuna K. Kothandath, Glenn C. Yiu, and 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
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KEYWORDS
Image segmentation

Optical coherence tomography

3D image processing

Diagnostics

Retina

Denoising

Eye

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