
Proceedings Paper
Robust non-local multi-atlas segmentation of the optic nerveFormat | Member Price | Non-Member Price |
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Paper Abstract
Labeling or segmentation of structures of interest on medical images plays an essential role in both clinical and scientific
understanding of the biological etiology, progression, and recurrence of pathological disorders. Here, we focus on the
optic nerve, a structure that plays a critical role in many devastating pathological conditions – including glaucoma,
ischemic neuropathy, optic neuritis and multiple-sclerosis. Ideally, existing fully automated procedures would result in
accurate and robust segmentation of the optic nerve anatomy. However, current segmentation procedures often require
manual intervention due to anatomical and imaging variability. Herein, we propose a framework for robust and fully-automated
segmentation of the optic nerve anatomy. First, we provide a robust registration procedure that results in
consistent registrations, despite highly varying data in terms of voxel resolution and image field-of-view. Additionally,
we demonstrate the efficacy of a recently proposed non-local label fusion algorithm that accounts for small scale errors
in registration correspondence. On a dataset consisting of 31 highly varying computed tomography (CT) images of the
human brain, we demonstrate that the proposed framework consistently results in accurate segmentations. In particular,
we show (1) that the proposed registration procedure results in robust registrations of the optic nerve anatomy, and (2)
that the non-local statistical fusion algorithm significantly outperforms several of the state-of-the-art label fusion
algorithms.
Paper Details
Date Published: 13 March 2013
PDF: 8 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86691L (13 March 2013); doi: 10.1117/12.2007015
Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)
PDF: 8 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86691L (13 March 2013); doi: 10.1117/12.2007015
Show Author Affiliations
Andrew J. Asman, Vanderbilt Univ. (United States)
Michael P. DeLisi, Vanderbilt Univ. (United States)
Louise A. Mawn, Vanderbilt Univ. (United States)
Michael P. DeLisi, Vanderbilt Univ. (United States)
Louise A. Mawn, Vanderbilt Univ. (United States)
Robert L. Galloway, Vanderbilt Univ. (United States)
Bennett A. Landman, Vanderbilt Univ. (United States)
Bennett A. Landman, Vanderbilt Univ. (United States)
Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)
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