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Proceedings Paper

Automated epicardial fat volume quantification from non-contrast CT
Author(s): Xiaowei Ding; Demetri Terzopoulos; Mariana Diaz-Zamudio; Daniel S. Berman; Piotr J. Slomka; Damini Dey
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Paper Abstract

Epicardial fat volume (EFV) is now regarded as a significant imaging biomarker for cardiovascular risk strat-ification. Manual or semi-automated quantification of EFV includes tedious and careful contour drawing of pericardium on fine image features. We aimed to develop and validate a fully-automated, accurate algorithm for EVF quantification from non-contrast CT using active contours and multiple atlases registration. This is a knowledge-based model that can segment both the heart and pericardium accurately by initializing the location and shape of the heart in large scale from multiple co-registered atlases and locking itself onto the pericardium actively. The deformation process is driven by pericardium detection, extracting only the white contours repre- senting the pericardium in the CT images. Following this step, we can calculate fat volume within this region (epicardial fat) using standard fat attenuation range. We validate our algorithm on CT datasets from 15 patients who underwent routine assessment of coronary calcium. Epicardial fat volume quantified by the algorithm (69.15 ± 8.25 cm3) and the expert (69.46 ± 8.80 cm3) showed excellent correlation (r = 0.96, p < 0.0001) with no significant differences by comparison of individual data points (p = 0.9). The algorithm achieved a Dice overlap of 0.93 (range 0.88 - 0.95). The total time was less than 60 sec on a standard windows computer. Our results show that fast accurate automated knowledge-based quantification of epicardial fat volume from non-contrast CT is feasible. To our knowledge, this is also the first fully automated algorithms reported for this task.

Paper Details

Date Published: 21 March 2014
PDF: 6 pages
Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90340I (21 March 2014); doi: 10.1117/12.2043326
Show Author Affiliations
Xiaowei Ding, Cedars-Sinai Medical Ctr. (United States)
Univ. of California, Los Angeles (United States)
Demetri Terzopoulos, Univ. of California, Los Angeles (United States)
Mariana Diaz-Zamudio, Cedars Sinai Medical Ctr. (United States)
Daniel S. Berman, Cedars-Sinai Medical Ctr. (United States)
Univ. of California, Los Angeles (United States)
Piotr J. Slomka, Cedars-Sinai Medical Ctr. (United States)
Univ. of California, Los Angeles (United States)
Damini Dey, Cedars-Sinai Medical Ctr. (United States)
Univ. of California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 9034:
Medical Imaging 2014: Image Processing
Sebastien Ourselin; Martin A. Styner, Editor(s)

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