  The deposits of fat on the surroundings of the heart are correlated to
several health risk factors such as atherosclerosis, carotid stiffness,
coronary artery calcification, atrial fibrillation and many others. These
deposits vary unrelated to obesity, which reinforces its direct segmentation
for further quantification. However, manual segmentation of these fats has not
been widely deployed in clinical practice due to the required human workload
and consequential high cost of physicians and technicians. In this work, we
propose a unified method for an autonomous segmentation and quantification of
two types of cardiac fats. The segmented fats are termed epicardial and
mediastinal, and stand apart from each other by the pericardium. Much effort
was devoted to achieve minimal user intervention. The proposed methodology
mainly comprises registration and classification algorithms to perform the
desired segmentation. We compare the performance of several classification
algorithms on this task, including neural networks, probabilistic models and
decision tree algorithms. Experimental results of the proposed methodology have
shown that the mean accuracy regarding both epicardial and mediastinal fats is
98.5% (99.5% if the features are normalized), with a mean true positive rate of
98.0%. In average, the Dice similarity index was equal to 97.6%.
