A novel approach for the automated segmentation and volume quantification of cardiac fats on computed tomography

TL;DR

This paper introduces an automated method for segmenting and quantifying cardiac fats in CT scans, achieving high accuracy and reducing manual effort, which could improve clinical assessment of cardiovascular risk factors.

Contribution

The study presents a unified, minimally supervised approach using registration and classification algorithms for accurate cardiac fat segmentation in CT images.

Findings

Achieved 98.5% mean accuracy in fat segmentation

Dice similarity index of 97.6% indicating high overlap with manual segmentation

High true positive rate of 98.0% for the proposed method

Abstract

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%.