  Background: Increased pericardial adipose tissue (PAT) is associated with
many types of cardiovascular disease (CVD). Although cardiac magnetic resonance
images (CMRI) are often acquired in patients with CVD, there are currently no
tools to automatically identify and quantify PAT from CMRI. The aim of this
study was to create a neural network to segment PAT from T2-weighted CMRI and
explore the correlations between PAT volumes (PATV) and CVD outcomes and
mortality. Methods: We trained and tested a deep learning model, PAT-CNN, to
segment PAT on T2-weighted cardiac MR images. Using the segmentations from
PAT-CNN, we automatically calculated PATV on images from 391 patients. We
analysed correlations between PATV and CVD diagnosis and 1-year mortality
post-imaging. Results: PAT-CNN was able to accurately segment PAT with Dice
score/ Hausdorff distances of 0.74 +- 0.03/27.1 +- 10.9~mm, similar to the
values obtained when comparing the segmentations of two independent human
observers ($0.76 +- 0.06/21.2 +- 10.3~mm$). Regression models showed that,
independently of sex and body-mass index, PATV is significantly positively
correlated 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 images
automatically and accurately. Increased PATV as measured automatically from
CMRI is significantly associated with the presence of CVD and can independently
predict 1-year mortality.
