Abstract #2648

Deep-Learning epicardial fat quantification using 4-chambers Cardiac MRI segmentation, comparison with total epicardial fat volume

Pierre Daudé¹, Patricia Ancel², Sylviane Confort-gouny¹, Anne Dutour², Bénédicte Gaborit², and Stanislas Rapacchi¹

¹Aix-Marseille Univ, CNRS, CRMBM, Marseille, France, ²APHM, Hôpital Universitaire Timone, Service d’Endocrinologie, Marseille, France

Evaluation of epicardial adipose tissue (EAT) burden holds potential as a biomarker for CHD diagnosis. EAT volume is challenging to assess using MRI due to its curved shape susceptible to partial volume effect. As a substitute, 4-chamber EAT surface can be reliably measured and has shown good correlation with EAT volume (r²=0.62). Two fully convolutional neural networks (FCN) were investigated for the segmentation of EAT surface on a database of 126 subjects. Promising results were obtained with DICE values of 0.71.

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