10 March 2006 Automatic cardiac MRI myocardium segmentation using graphcut
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Proceedings Volume 6144, Medical Imaging 2006: Image Processing; 61440A (2006); doi: 10.1117/12.653583
Event: Medical Imaging, 2006, San Diego, California, United States
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.
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Gunnar Kedenburg, Chris A. Cocosco, Ullrich Köthe, Wiro J. Niessen, Evert-jan P. A. Vonken, Max A. Viergever, "Automatic cardiac MRI myocardium segmentation using graphcut", Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 61440A (10 March 2006); doi: 10.1117/12.653583; https://doi.org/10.1117/12.653583
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KEYWORDS
Image segmentation

Fuzzy logic

Cardiovascular magnetic resonance imaging

Magnetic resonance imaging

Tissues

Lung

Blood

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