Segmentation of epicardial adipose tissue in cardiac MRI using deep learning

Abstract

Epicardial adipose tissue (EAT) is the layer of fat that accumulates around the myocardium of the heart and is a contributing factor to cardiovascular disease. Identification and quantification of this fat depot is important in ongoing studies of intervention. We have manually traced the EAT in 20 cardiac MRI scans, but this process is tedious and time-consuming. The goal of this project was to develop a segmentation algorithm that would shorten the time it takes to quantify the EAT. The validation data consisted of pre-intervention and post-intervention MRI scans from 12 (4 subjects did not have post-intervention scans) volunteer female subjects. The EAT, myocardium, and ventricles were manually traced in each slice of each scan. For the automated algorithm, preprocessing consisted of transforming the image data to the polar domain using the centroid of the traced inner EAT contour. In the polar image, each radial angle contained an inner-contour point and an outer-contour point, identifying the thickness of the fat at that radial location in that slice. These two locations on each single angle view served as the input for the neural network along with the angle, the slice location, and time in the cardiac cycle including either end-diastole or end-systole. Two neural networks were trained, one for the inner edge of EAT and a second for the outer edge of EAT. The networks returned the location of the contours in each radial angle and this was compared with the traced solutions. The mean dice similarity coefficient for the automatically identified EAT vs. the manually traced EAT was 0.56 ± 0.12. The current algorithm produces promising results that warrant further investigation and development.