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.

翻译：心脏周围脂肪库的量化是评价与若干疾病相关的健康风险因素的准确程序。然而,由于需要人的工作量,这种评价在临床实践中没有被广泛采用。这项工作提出了心脏脂肪垫的自动分解新技术。该技术的基础是对心脏CT图像的分解应用分类算法。此外,我们广泛评价了这项任务的若干算法的性能,并讨论了这些算法提供了更好的预测模型。实验结果显示,震动和中间脂肪分类的平均准确性为98.4%,平均真实正率为96.2%。平均而言,关于分解病人和地面真相的骰子相似性指数为96.8%。我们的技术迄今为止在心脏脂肪的自动分解方面已经取得了最准确的结果。