Automatic quantification of myocardium and pericardial fat from coronary computed tomography angiography: a multicenter study

• Published in: European radiology Vol. 31; no. 6; pp. 3826 - 3836
• Main Authors: He, Xiuxiu, Guo, Bang Jun, Lei, Yang, Wang, Tonghe, Curran, Walter J., Liu, Tian, Zhang, Long Jiang, Yang, Xiaofeng
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-06-2021; Springer Nature B.V
• Subjects: Accuracy; Angiography; Annotations; Cardiovascular diseases; Computed tomography; Deep learning; Diagnostic Radiology; Heart diseases; Imaging; Imaging Informatics and Artificial Intelligence; Internal Medicine; Interventional Radiology; Medical imaging; Medical treatment; Medicine; Medicine & Public Health; Metric space; Myocardium; Neuroradiology; Performance evaluation; Radiology; Segmentation; Simultaneous discrimination learning; Training; Ultrasound; Deep learning; Computed tomography angiography; Computer-assisted image processing; Cardiovascular disease
• ISSN: 0938-7994; 1432-1084
• DOI: 10.1007/s00330-020-07482-5

Objectives
To develop a deep learning–based method for simultaneous myocardium and pericardial fat quantification from coronary computed tomography angiography (CCTA) for the diagnosis and treatment of cardiovascular disease (CVD).
Methods
We retrospectively identified CCTA data obtained between May 2008 and July 2018 in a multicenter (six centers) CVD study. The proposed method was evaluated on 422 patients’ data by two studies. The first overall study involves training model on CVD patients and testing on non-CVD patients, as well as training on non-CVD patients and testing on CVD patients. The second study was performed using the leave-center-out approach. The method performance was evaluated using Dice similarity coefficient (DSC), Jaccard index (JAC), 95% Hausdorff distance (HD95), mean surface distance (MSD), residual mean square distance (RMSD), and the center of mass distance (CMD). The robustness of the proposed method was tested using the nonparametric Kruskal-Wallis test and post hoc test to assess the equality of distribution of DSC values among different tests.
Results
The automatic segmentation achieved a strong correlation with contour (ICC and
R
 > 0.97,
p
value < 0.001 throughout all tests). The accuracy of the proposed method remained high through all the tests, with the median DSC higher than 0.88 for pericardial fat and 0.96 for myocardium. The proposed method also resulted in mean MSD, RMSD, HD95, and CMD of less than 1.36 mm for pericardial fat and 1.00 mm for myocardium.
Conclusions
The proposed deep learning–based segmentation method enables accurate simultaneous quantification of myocardium and pericardial fat in a multicenter study.
Key Points
•
Deep learning–based myocardium and pericardial fat segmentation method tested on 422 patients’ coronary computed tomography angiography in a multicenter study.
•
The proposed method provides segmentations with high volumetric accuracy (ICC and R > 0.97, p value < 0.001) and similar shape as manual annotation by experienced radiologists (median Dice similarity coefficient ≥ 0.88 for pericardial fat and 0.96 for myocardium).