Automated pericardium delineation and epicardial fat volume quantification from noncontrast CT.
OBJECTIVE
The authors aimed to develop and validate an automated algorithm for epicardial fat volume (EFV) quantification from noncontrast CT.


METHODS
The authors developed a hybrid algorithm based on initial segmentation with a multiple-patient CT atlas, followed by automated pericardium delineation using geodesic active contours. A coregistered segmented CT atlas was created from manually segmented CT data and stored offline. The heart and pericardium in test CT data are first initialized by image registration to the CT atlas. The pericardium is then detected by a knowledge-based algorithm, which extracts only the membrane representing the pericardium. From its initial atlas position, the pericardium is modeled by geodesic active contours, which iteratively deform and lock onto the detected pericardium. EFV is automatically computed using standard fat attenuation range.


RESULTS
The authors applied their algorithm on 50 patients undergoing routine coronary calcium assessment by CT. Measurement time was 60 s per-patient. EFV quantified by the algorithm (83.60 ± 32.89 cm(3)) and expert readers (81.85 ± 34.28 cm(3)) showed excellent correlation (r = 0.97, p < 0.0001), with no significant differences by comparison of individual data points (p = 0.15). Voxel overlap by Dice coefficient between the algorithm and expert readers was 0.92 (range 0.88-0.95). The mean surface distance and Hausdorff distance in millimeter between manually drawn contours and the automatically obtained contours were 0.6 ± 0.9 mm and 3.9 ± 1.7 mm, respectively. Mean difference between the algorithm and experts was 9.7% ± 7.4%, similar to interobserver variability between 2 readers (8.0% ± 5.3%, p = 0.3).


CONCLUSIONS
The authors' novel automated method based on atlas-initialized active contours accurately and rapidly quantifies EFV from noncontrast CT.
Biomedical Imaging Research Institute, Department of Biomedical Sciences, Cedars Sinai Medical Center, Los Angeles, California 90048 and Computer Science Department, Henry Samueli School of Engineering and Applied Science at UCLA, Los Angeles, California 90095.
Xiaowei
X
Ding
Computer Science Department, Henry Samueli School of Engineering and Applied Science at UCLA, Los Angeles, California 90095.
Demetri
D
Terzopoulos
Nuclear Medicine Department, Cedars Sinai Medical Center, Los Angeles, California 90048.
Mariana
M
Diaz-Zamudio
Departments of Imaging and Medicine, Cedars Sinai Medical Center, Los Angeles, California 90048 and Department of Medicine, David-Geffen School of Medicine at UCLA, Los Angeles, California 90095.
Daniel S
DS
Berman
Departments of Imaging and Medicine, Cedars Sinai Medical Center, Los Angeles, California 90048 and Department of Medicine, David-Geffen School of Medicine at UCLA, Los Angeles, California 90095.
Piotr J
PJ
Slomka
Biomedical Imaging Research Institute, Department of Biomedical Sciences, Cedars Sinai Medical Center, Los Angeles, California 90048 and Department of Medicine, David-Geffen School of Medicine at UCLA, Los Angeles, California 90095.
Damini
D
Dey
D000273:Adipose Tissue / Q000166:cytology* / Q000000981:diagnostic imaging*; D000465:Algorithms; D001331:Automation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D010496:Pericardium / Q000166:cytology* / Q000000981:diagnostic imaging; D014057:Tomography, X-Ray Computed