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Proceedings Paper

Automatic cardiac MRI myocardium segmentation using graphcut
Author(s): Gunnar Kedenburg; Chris A. Cocosco; Ullrich Köthe; Wiro J. Niessen; Evert-jan P. A. Vonken; Max A. Viergever
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Paper Abstract

Segmentation of the left myocardium in four-dimensional (space-time) cardiac MRI data sets is a prerequisite of many diagnostic tasks. We propose a fully automatic method based on global minimization of an energy functional by means of the graphcut algorithm. Starting from automatically obtained segmentations of the left and right ventricles and a cardiac region of interest, a spatial model is constructed using simple and plausible assumptions. This model is used to learn the appearance of different tissue types by non parametric robust estimation. Our method does not require previously trained shape or appearance models. Processing takes 30-40s on current hardware. We evaluated our method on 11 clinical cardiac MRI data sets acquired using cine balanced fast field echo. Linear regression of the automatically segmented myocardium volume against manual segmentations (performed by a radiologist) showed an RMS error of about 12ml.

Paper Details

Date Published: 10 March 2006
PDF: 12 pages
Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 61440A (10 March 2006); doi: 10.1117/12.653583
Show Author Affiliations
Gunnar Kedenburg, Univ. of Hamburg (Germany)
Chris A. Cocosco, Philips Research Labs. (Germany)
Ullrich Köthe, Univ. of Hamburg (Germany)
Wiro J. Niessen, Erasmus Medical Ctr. (Netherlands)
Evert-jan P. A. Vonken, Univ. Medical Ctr. Utrecht (Netherlands)
Max A. Viergever, Univ. Medical Ctr. Utrecht (Netherlands)

Published in SPIE Proceedings Vol. 6144:
Medical Imaging 2006: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

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