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Proceedings Paper

Optimal retinal cyst segmentation from OCT images
Author(s): Ipek Oguz; Li Zhang; Michael D. Abràmoff; Milan Sonka
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Paper Abstract

Accurate and reproducible segmentation of cysts and fluid-filled regions from retinal OCT images is an important step allowing quantification of the disease status, longitudinal disease progression, and response to therapy in wet-pathology retinal diseases. However, segmentation of fluid-filled regions from OCT images is a challenging task due to their inhomogeneous appearance, the unpredictability of their number, size and location, as well as the intensity profile similarity between such regions and certain healthy tissue types. While machine learning techniques can be beneficial for this task, they require large training datasets and are often over-fitted to the appearance models of specific scanner vendors. We propose a knowledge-based approach that leverages a carefully designed cost function and graph-based segmentation techniques to provide a vendor-independent solution to this problem. We illustrate the results of this approach on two publicly available datasets with a variety of scanner vendors and retinal disease status. Compared to a previous machine-learning based approach, the volume similarity error was dramatically reduced from 81:3±56:4% to 22:2±21:3% (paired t-test, p << 0:001).

Paper Details

Date Published: 21 March 2016
PDF: 7 pages
Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97841E (21 March 2016); doi: 10.1117/12.2217355
Show Author Affiliations
Ipek Oguz, The Univ. of Iowa (United States)
Li Zhang, The Univ. of Iowa (United States)
Michael D. Abràmoff, The Univ. of Iowa (United States)
U.S. Dept. of Veteran Affairs (United States)
Milan Sonka, The Univ. of Iowa (United States)

Published in SPIE Proceedings Vol. 9784:
Medical Imaging 2016: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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