Deep learning segmentation and quantification method for assessing epicardial adipose tissue in CT calcium score scans.

ABSTRACT: Epicardial adipose tissue volume (EAT) has been linked to coronary artery disease and the risk of major adverse cardiac events. As manual quantification of EAT is time-consuming, requires specialized training, and is prone to human error, we developed a deep learning method (DeepFat) for the automatic assessment of EAT on non-contrast low-dose CT calcium score images. Our DeepFat intuitively segmented the tissue enclosed by the pericardial sac on axial slices, using two preprocessing steps. First, we applied a HU-attention-window with a window/level 350/40-HU to draw attention to the sac and reduce numerical errors. Second, we applied a novel look ahead slab-of-slices with bisection ("bisect") in which we split the heart into halves and sequenced the lower half from bottom-to-middle and th

SUBMITTER: Hoori A 

PROVIDER: S-EPMC8831577 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature