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Automatic pericardium segmentation and quantification of epicardial fat from computed tomography angiography

Automatic pericardium segmentation and quantification of epicardial fat from computed tomography... Abstract. Recent findings indicate a strong correlation between the risk of future heart disease and the volume of adipose tissue inside of the pericardium. So far, large-scale studies have been hindered by the fact that manual delineation of the pericardium is extremely time-consuming and that existing methods for automatic delineation lack accuracy. An efficient and fully automatic approach to pericardium segmentation and epicardial fat volume (EFV) estimation is presented, based on a variant of multi-atlas segmentation for spatial initialization and a random forest classifier for accurate pericardium detection. Experimental validation on a set of 30 manually delineated computer tomography angiography volumes shows a significant improvement on state-of-the-art in terms of EFV estimation (mean absolute EFV difference: 3.8 ml (4.7%), Pearson correlation: 0.99) with run times suitable for large-scale studies (52 s). Further, the results compare favorably with interobserver variability measured on 10 volumes. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Medical Imaging SPIE

Automatic pericardium segmentation and quantification of epicardial fat from computed tomography angiography

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Publisher
SPIE
Copyright
© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)
Subject
Image Processing; Paper
ISSN
2329-4302
eISSN
2329-4310
DOI
10.1117/1.JMI.3.3.034003
pmid
27660804
Publisher site
See Article on Publisher Site

Abstract

Abstract. Recent findings indicate a strong correlation between the risk of future heart disease and the volume of adipose tissue inside of the pericardium. So far, large-scale studies have been hindered by the fact that manual delineation of the pericardium is extremely time-consuming and that existing methods for automatic delineation lack accuracy. An efficient and fully automatic approach to pericardium segmentation and epicardial fat volume (EFV) estimation is presented, based on a variant of multi-atlas segmentation for spatial initialization and a random forest classifier for accurate pericardium detection. Experimental validation on a set of 30 manually delineated computer tomography angiography volumes shows a significant improvement on state-of-the-art in terms of EFV estimation (mean absolute EFV difference: 3.8 ml (4.7%), Pearson correlation: 0.99) with run times suitable for large-scale studies (52 s). Further, the results compare favorably with interobserver variability measured on 10 volumes.

Journal

Journal of Medical ImagingSPIE

Published: Jul 1, 2016

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