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Proceedings Paper

Automated segmentation of cardiac visceral fat in low-dose non-contrast chest CT images
Author(s): Yiting Xie; Mingzhu Liang; David F. Yankelevitz; Claudia I. Henschke; Anthony P. Reeves
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Paper Abstract

Cardiac visceral fat was segmented from low-dose non-contrast chest CT images using a fully automated method. Cardiac visceral fat is defined as the fatty tissues surrounding the heart region, enclosed by the lungs and posterior to the sternum. It is measured by constraining the heart region with an Anatomy Label Map that contains robust segmentations of the lungs and other major organs and estimating the fatty tissue within this region. The algorithm was evaluated on 124 low-dose and 223 standard-dose non-contrast chest CT scans from two public datasets. Based on visual inspection, 343 cases had good cardiac visceral fat segmentation. For quantitative evaluation, manual markings of cardiac visceral fat regions were made in 3 image slices for 45 low-dose scans and the Dice similarity coefficient (DSC) was computed. The automated algorithm achieved an average DSC of 0.93. Cardiac visceral fat volume (CVFV), heart region volume (HRV) and their ratio were computed for each case. The correlation between cardiac visceral fat measurement and coronary artery and aortic calcification was also evaluated. Results indicated the automated algorithm for measuring cardiac visceral fat volume may be an alternative method to the traditional manual assessment of thoracic region fat content in the assessment of cardiovascular disease risk.

Paper Details

Date Published: 20 March 2015
PDF: 8 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94140G (20 March 2015); doi: 10.1117/12.2081959
Show Author Affiliations
Yiting Xie, Cornell Univ. (United States)
Mingzhu Liang, Icahn School of Medicine at Mount Sinai (United States)
David F. Yankelevitz, Icahn School of Medicine at Mount Sinai (United States)
Claudia I. Henschke, Icahn School of Medicine at Mount Sinai (United States)
Anthony P. Reeves, Cornell Univ. (United States)


Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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