Using the U-Net Family for Epicardial Adipose Tissue Segmentation and Quantification in Non-Contrast CT

Background: Epicardial adipose tissue (EAT) locates between the visceral pericardium and myocardium and the EAT volume is correlated with cardiovascular risk. Nowadays, many deep learning-based automated EAT segmentation and quantification methods in the U-net family were developed to reduce the workload for radiologists. However, most of the works were based on private or small dataset with di↵erent label types. Thus, their reproducibility is relatively low and comparison of their performance is difficult. Methods: In this work, we comparably studied and evaluated the state-of-the-art segmentation methods, and o↵ered future directions. Our work is based on a dataset of 154 non-contrast CT scans from the ROBINSCA study with two types of labels: (a) region inside the pericardium and (b) pixel-wise EAT labels. We selected four advanced methods from the U-net family: 3D U-net, 3D attention U-net, an extended 3D attention U-net, and U-net++. For evaluation, we did both four-fold cross-validation and hold-out tests. Agreement between the automatic segmentation/quantification and the manual quantification was evaluated with the Pearson correlation and the Bland-Altman analysis.Results: Generally, the models trained with label type (a) showed better performance compared to models trained with label type (b). The U-net++ model trained with label type (a) showed the best performance of segmentation and quantification. The U-net++ model trained with label type (a) efficiently provides better EAT segmentation results(Hold-out test: DCS=80.18 ± 0.20%, mIoU=67.13 ± 0.39%, sensitivity=81.47 ± 0.43%, specificity=99.64 ± 0.00%, Pearson correlation=0.9405) and EAT volume compared to the other U-net-based networks and the recent EAT segmentation method.Conclusions: 3D convolutional neural networks do not always perform better than 2D convolutional neural networks in the EAT segmentation and quantification.And labels of the region inside the pericardium are helpful to train more accurate EAT segmentation models. Deep learning-based methods have the potential to provide good EAT segmentation and quantification.