  The quantification of fat depots on the surroundings of the heart is an
accurate procedure for evaluating health risk factors correlated with several
diseases. However, this type of evaluation is not widely employed in clinical
practice due to the required human workload. This work proposes a novel
technique for the automatic segmentation of cardiac fat pads. The technique is
based on applying classification algorithms to the segmentation of cardiac CT
images. Furthermore, we extensively evaluate the performance of several
algorithms on this task and discuss which provided better predictive models.
Experimental results have shown that the mean accuracy for the classification
of epicardial and mediastinal fats has been 98.4% with a mean true positive
rate of 96.2%. On average, the Dice similarity index, regarding the segmented
patients and the ground truth, was equal to 96.8%. Therfore, our technique has
achieved the most accurate results for the automatic segmentation of cardiac
fats, to date.
