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Proceedings Paper

A fully automatic framework for cell segmentation on non-confocal adaptive optics images
Author(s): Jianfei Liu; Alfredo Dubra; Johnny Tam
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Paper Abstract

By the time most retinal diseases are diagnosed, macroscopic irreversible cellular loss has already occurred. Earlier detection of subtle structural changes at the single photoreceptor level is now possible, using the adaptive optics scanning light ophthalmoscope (AOSLO). This work aims to develop a fully automatic segmentation framework to extract cell boundaries from non-confocal split-detection AOSLO images of the cone photoreceptor mosaic in the living human eye. Significant challenges include anisotropy, heterogeneous cell regions arising from shading effects, and low contrast between cells and background. To overcome these challenges, we propose the use of: 1) multi-scale Hessian response to detect heterogeneous cell regions, 2) convex hulls to create boundary templates, and 3) circularlyconstrained geodesic active contours to refine cell boundaries. We acquired images from three healthy subjects at eccentric retinal regions and manually contoured cells to generate ground-truth for evaluating segmentation accuracy. Dice coefficient, relative absolute area difference, and average contour distance were 82±2%, 11±6%, and 2.0±0.2 pixels (Mean±SD), respectively. We find that strong shading effects from vessels are a main factor that causes cell oversegmentation and false segmentation of non-cell regions. Our segmentation algorithm can automatically and accurately segment photoreceptor cells on non-confocal AOSLO images, which is the first step in longitudinal tracking of cellular changes in the individual eye over the time course of disease progression.

Paper Details

Date Published: 24 March 2016
PDF: 7 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97852J (24 March 2016); doi: 10.1117/12.2217191
Show Author Affiliations
Jianfei Liu, National Institutes of Health (United States)
Alfredo Dubra, Medical College of Wisconsin (United States)
Johnny Tam, National Institutes of Health (United States)

Published in SPIE Proceedings Vol. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato III, Editor(s)

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