  Epicardial adipose tissue is a type of adipose tissue located between the
heart wall and a protective layer around the heart called the pericardium. The
volume and thickness of epicardial adipose tissue are linked to various
cardiovascular diseases. It is shown to be an independent cardiovascular
disease risk factor. Fully automatic and reliable measurements of epicardial
adipose tissue from CT scans could provide better disease risk assessment and
enable the processing of large CT image data sets for a systemic epicardial
adipose tissue study. This paper proposes a method for fully automatic semantic
segmentation of epicardial adipose tissue from CT images using a deep neural
network. The proposed network uses a U-Net-based architecture with slice depth
information embedded in the input image to segment a pericardium region of
interest, which is used to obtain an epicardial adipose tissue segmentation.
Image augmentation is used to increase model robustness. Cross-validation of
the proposed method yields a Dice score of 0.86 on the CT scans of 20 patients.
