Fully Automated CT Quantification of Epicardial Adipose Tissue by Deep Learning

作者：F. Commandeur, M. Goeller, A. Razipour, S. Cadet, M. Hell, J. Kwieciński, Xi Chen, H. Chang, M. Marwan, S. Achenbach, D. Berman, P. Slomka, B. Tamarappoo, D. Dey · 发表于：Radiology: Artificial Intelligence · 年份：2019 · DOI：10.1148/ryai.2019190045 · 被引用次数：123 · 研究领域：Medicine

Purpose: To evaluate the performance of deep learning for robust and fully automated quantification of epicardial adipose tissue (EAT) from multicenter cardiac CT data. Materials and Methods: In this multicenter study, a convolutional neural network approach was trained to quantify EAT on non–contrast material–enhanced calcium-scoring CT scans from multiple cohorts, scanners, and protocols (n = 850). Deep learning performance was compared with the performance of three expert readers and with interobserver variability in a subset of 141 scans. The deep learning algorithm was incorporated into research software. Automated EAT progression was compared with expert measurements for 70 patients with baseline and follow-up scans. Results: Automated quantification was performed in a mean (± standard deviation) time of 1.57 seconds ± 0.49, compared with 15 minutes for experts. Deep learning provided high agreement with expert manual quantification for all scans (R = 0.974; P < .001), with no significant bias (0.53 cm3; P = .13). Manual EAT volumes measured by two experienced readers were highly correlated (R = 0.984; P < .001) but with a bias of 4.35 cm3 (P < .001). Deep learning quantifications were highly correlated with the measurements of both experts (R = 0.973 and R = 0.979; P < .001), with significant bias for reader 1 (5.11 cm3; P < .001) but not for reader 2 (0.88 cm3; P = .26). EAT progression by deep learning correlated strongly with manual EAT progression (R = 0.905; P < ....