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

Alexander Norlén (Institutionen för signaler och system, Bildanalys och datorseende) ; Jennifer Alvén (Institutionen för signaler och system, Bildanalys och datorseende) ; David Molnar ; Olof Enqvist ; Rauni Rossi-Norrlund ; John Brandberg ; Göran Bergström ; Fredrik Kahl (Institutionen för signaler och system, Bildanalys och datorseende)
Journal of Mecial Imaging (2329-4302). Vol. 3 (2016), 3, p. Article number 034003.
[Artikel, refereegranskad vetenskaplig]

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.

Nyckelord: computed tomography angiography (CTA), segmentation, machine learning, epicardial fat quantification,pericardium



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Denna post skapades 2016-09-15. Senast ändrad 2016-11-07.
CPL Pubid: 241847

 

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