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Proceedings Paper

Incorporation of learned shape priors into a graph-theoretic approach with application to the 3D segmentation of intraretinal surfaces in SD-OCT volumes of mice
Author(s): Bhavna J. Antony; Qi Song; Michael D. Abràmoff; Eliott Sohn; Xiaodong Wu; Mona K. Garvin
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Paper Abstract

Spectral-domain optical coherence tomography (SD-OCT) finds widespread use clinically for the detection and management of ocular diseases. This non-invasive imaging modality has also begun to find frequent use in research studies involving animals such as mice. Numerous approaches have been proposed for the segmentation of retinal surfaces in SD-OCT images obtained from human subjects; however, the segmentation of retinal surfaces in mice scans is not as well-studied. In this work, we describe a graph-theoretic segmentation approach for the simultaneous segmentation of 10 retinal surfaces in SD-OCT scans of mice that incorporates learned shape priors. We compared the method to a baseline approach that did not incorporate learned shape priors and observed that the overall unsigned border position errors reduced from 3.58 +/- 1.33 μm to 3.20 +/- 0.56 μm.

Paper Details

Date Published: 13 March 2014
PDF: 10 pages
Proc. SPIE 9038, Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging, 90380D (13 March 2014); doi: 10.1117/12.2043203
Show Author Affiliations
Bhavna J. Antony, The Univ. of Iowa (United States)
Qi Song, GE Global Research (United States)
Michael D. Abràmoff, The Univ. of Iowa (United States)
Iowa City VA Health Care System (United States)
Eliott Sohn, The Univ. of Iowa (United States)
Xiaodong Wu, The Univ. of Iowa (United States)
Mona K. Garvin, Iowa City VA Health Care System (United States)
The Univ. of Iowa (United States)


Published in SPIE Proceedings Vol. 9038:
Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging
Robert C. Molthen; John B. Weaver, Editor(s)

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