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

Journal article
Authors Alexander Norlén
Jennifer Alvén
David Molnar
Olof Enqvist
Rauni Rossi-Norrlund
John Brandberg
Göran Bergström
Fredrik Kahl
Published in Journal of Mecial Imaging
Volume 3
Issue 3
ISSN 2329-4302
Publication year 2016
Published at Wallenberg Laboratory
Language en
Keywords computed tomography angiography (CTA), segmentation, machine learning, epicardial fat quantification,pericardium
Subject categories Computer Vision and Robotics (Autonomous Systems), Image analysis, Medical Engineering, Medical Image Processing


Recent findings indicate a strong correlation between the risk of future heart disease and the volume ofadipose tissue inside of the pericardium. So far, large-scale studies have been hindered by the fact that manual delin-eation of the pericardium is extremely time-consuming and that existing methods for automatic delineation strugglewith accuracy. In this paper, an efficient and fully automatic approach to pericardium segmentation and epicardial fatvolume estimation is presented, based on a variant of multi-atlas segmentation for spatial initialization and a randomforest classifier for accurate pericardium detection. Experimental validation on a set of 30 manually delineated Com-puter Tomography Angiography (CTA) volumes shows a significant improvement on state-of-the-art in terms of EFVestimation (mean absolute epicardial fat volume difference: 3.8 ml (4.7%), Pearson correlation: 0.99) with run-timessuitable for large-scale studies (52 s). Further, the results compare favorably to inter-observer variability measured on10 volumes.

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