15 September 2016 Automatic pericardium segmentation and quantification of epicardial fat from computed tomography angiography
Author Affiliations +
J. of Medical Imaging, 3(3), 034003 (2016). doi:10.1117/1.JMI.3.3.034003
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.
© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)
Alexander Norlén, Jennifer Alvén, David Molnar, Olof Enqvist, Rauni Rossi Norrlund, John Brandberg, Göran Bergström, Fredrik Kahl, "Automatic pericardium segmentation and quantification of epicardial fat from computed tomography angiography," Journal of Medical Imaging 3(3), 034003 (15 September 2016). https://doi.org/10.1117/1.JMI.3.3.034003

Image segmentation

Computed tomography

Detection and tracking algorithms


Image registration

Image processing algorithms and systems


Back to Top