Deep Learning for Quantification of Epicardial and Thoracic Adipose Tissue from Non-Contrast CT
Abstract-Epicardial adipose tissue (EAT) is a visceral fat deposit related to coronary artery disease. Fully automated quantification of EAT volume in clinical routine could be a timesaving and reliable tool for cardiovascular risk assessment. We propose a new fully automated deep learning framework for EAT and thoracic adipose tissue (TAT) quantification from noncontrast coronary artery calcium CT scans. A first multi-task convolutional neural network (ConvNet) is used to determine heart limits and perform segmentation of heart and adipose tissues. A second ConvNet, combined with a statistical shape model (SSM), allows for pericardium detection. EAT and TAT segmentations are then obtained from outputs of both ConvNets. We evaluate the performance of the method on CT datasets from 250 asymptomatic individuals. Strong agreement between automatic and expert manual quantification is obtained for both EAT and TAT with median Dice score coefficients (DSC) of 0.823 (inter-quartile range (IQR): 0.779-0.860) and 0.905 (IQR: 0.8620.928), respectively; with excellent correlations of . and . for EAT and TAT volumes. Computations are performed in <26 seconds on a standard personal computer for one CT scan. Therefore, the proposed method may represent a tool for rapid fully automated quantification of adipose tissue and may improve cardiovascular risk stratification in patients referred for routine CT calcium scans.
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