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Background: Increased pericardial adipose tissue (PAT) is associated withmany types of cardiovascular disease (CVD). Although cardiac magnetic resonanceimages (CMRI) are often acquired in patients with CVD, there are currently notools to automatically identify and quantify PAT from CMRI. The aim of thisstudy was to create a neural network to segment PAT from T2-weighted CMRI andexplore the correlations between PAT volumes (PATV) and CVD outcomes andmortality. Methods: We trained and tested a deep learning model, PAT-CNN, tosegment PAT on T2-weighted cardiac MR images. Using the segmentations fromPAT-CNN, we automatically calculated PATV on images from 391 patients. Weanalysed correlations between PATV and CVD diagnosis and 1-year mortalitypost-imaging. Results: PAT-CNN was able to accurately segment PAT with Dicescore/ Hausdorff distances of 0.74 +- 0.03/27.1 +- 10.9~mm, similar to thevalues obtained when comparing the segmentations of two independent humanobservers ($0.76 +- 0.06/21.2 +- 10.3~mm$). Regression models showed that,independently of sex and body-mass index, PATV is significantly positivelycorrelated with a diagnosis of CVD and with 1-year all cause mortality (p-value< 0.01). Conclusions: PAT-CNN can segment PAT from T2-weighted CMR imagesautomatically and accurately. Increased PATV as measured automatically fromCMRI is significantly associated with the presence of CVD and can independentlypredict 1-year mortality.

PAT-CNN: Automatic Segmentation and Quantification of Pericardial Adipose Tissue from T2-Weighted Cardiac Magnetic Resonance Images