Automated segmentation of cardiac visceral fat in low-dose non-contrast chest CT images

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.

[1]  Moyses Szklo,et al.  The association of pericardial fat with incident coronary heart disease: the Multi-Ethnic Study of Atherosclerosis (MESA). , 2009, The American journal of clinical nutrition.

[2]  Udo Hoffmann,et al.  Prevalence, Distribution, and Risk Factor Correlates of High Pericardial and Intrathoracic Fat Depots in the Framingham Heart Study , 2010, Circulation. Cardiovascular imaging.

[3]  Anthony P. Reeves,et al.  Automated coronary artery calcification detection on low-dose chest CT images , 2014, Medical Imaging.

[4]  Piotr J. Slomka,et al.  Increased pericardial fat volume measured from noncontrast CT predicts myocardial ischemia by SPECT. , 2010, JACC. Cardiovascular imaging.

[5]  Piotr J. Slomka,et al.  Automated algorithm for atlas-based segmentation of the heart and pericardium from non-contrast CT , 2010, Medical Imaging.

[6]  Anthony P. Reeves,et al.  Heart region segmentation from low-dose CT scans: an anatomy based approach , 2012, Medical Imaging.

[7]  Richard C. Pais,et al.  The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. , 2011, Medical physics.

[8]  Ioannis A. Kakadiaris,et al.  Knowledge-based quantification of pericardial fat in non-contrast CT data , 2010, Medical Imaging.

[9]  Anthony P. Reeves,et al.  Local noise estimation in low-dose chest CT images , 2013, International Journal of Computer Assisted Radiology and Surgery.

[10]  Anthony P. Reeves,et al.  Automated aortic calcification detection in low-dose chest CT images , 2014, Medical Imaging.

[11]  J. Takasu,et al.  Pericardial fat accumulation in men as a risk factor for coronary artery disease. , 2001, Atherosclerosis.

[12]  Piotr J. Slomka,et al.  Automated epicardial fat volume quantification from non-contrast CT , 2014, Medical Imaging.

[13]  I. Kakadiaris,et al.  Computer-aided non-contrast CT-based quantification of pericardial and thoracic fat and their associations with coronary calcium and Metabolic Syndrome. , 2010, Atherosclerosis.

[14]  Udo Hoffmann,et al.  Pericardial Fat, Visceral Abdominal Fat, Cardiovascular Disease Risk Factors, and Vascular Calcification in a Community-Based Sample: The Framingham Heart Study , 2008, Circulation.

[15]  B. Krauskopf,et al.  Proc of SPIE , 2003 .

[16]  D. Dey,et al.  Automated Quantitation of Pericardiac Fat From Noncontrast CT , 2008, Investigative radiology.