Keywords: Segmentation, Segmentation, Deep Learning，cardiac adipose tissue
Motivation: Epicardial and paracardial adipose tissues are important for cardiac disease diagnosis but hard to segment.
Goal(s): To develop an approach to segment the two different fat tissues in short-axis cine MRI.
Approach: A UNet-based network is proposed to segment the two adipose tissues. Multi-resolution and 3D convolution modules were added to use the motion information for segmentation.
Results: The motion information contained in the cine images can substantially help distinguish between the two adipose tissues.
Impact: The proposed modules can effectively and precisely segment fat tissues even when the pericardium is challenging to observe, suggesting its potential for clinical applications.
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