On the Automated Segmentation of Epicardial and Mediastinal Cardiac Adipose Tissues Using Classification Algorithms

TL;DR

This paper introduces a highly accurate automated method for segmenting cardiac fat tissues in CT images, aiming to facilitate clinical assessment of health risks associated with cardiac adipose tissues.

Contribution

The study presents a novel classification-based approach for automatic segmentation of cardiac fat depots, demonstrating superior accuracy over existing methods.

Findings

Achieved 98.4% classification accuracy

Mean true positive rate of 96.2%

Dice similarity index of 96.8%

Abstract

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.