Cardiac fat segmentation using computed tomography and an image-to-image conditional generative adversarial neural network

TL;DR

This paper introduces a deep learning method using a conditional GAN to automatically segment and quantify cardiac fat deposits in CT images, achieving high accuracy and real-time performance.

Contribution

The study applies a pix2pix conditional GAN architecture to cardiac fat segmentation, demonstrating superior accuracy and efficiency over existing methods.

Findings

Achieved 99.08% accuracy and 98.73% F1-score for epicardial fat segmentation.

Achieved 97.90% accuracy and 98.40% F1-score for mediastinal fat segmentation.

Segmented images in real time with improved performance compared to prior approaches.

Abstract

In recent years, research has highlighted the association between increased adipose tissue surrounding the human heart and elevated susceptibility to cardiovascular diseases such as atrial fibrillation and coronary heart disease. However, the manual segmentation of these fat deposits has not been widely implemented in clinical practice due to the substantial workload it entails for medical professionals and the associated costs. Consequently, the demand for more precise and time-efficient quantitative analysis has driven the emergence of novel computational methods for fat segmentation. This study presents a novel deep learning-based methodology that offers autonomous segmentation and quantification of two distinct types of cardiac fat deposits. The proposed approach leverages the pix2pix network, a generative conditional adversarial network primarily designed for image-to-image translation tasks. By applying this network architecture, we aim to investigate its efficacy in tackling the specific challenge of cardiac fat segmentation, despite not being originally tailored for this purpose. The two types of fat deposits of interest in this study are referred to as epicardial and mediastinal fats, which are spatially separated by the pericardium. The experimental results demonstrated an average accuracy of 99.08% and f1-score 98.73 for the segmentation of the epicardial fat and 97.90% of accuracy and f1-score of 98.40 for the mediastinal fat. These findings represent the high precision and overlap agreement achieved by the proposed methodology. In comparison to existing studies, our approach exhibited superior performance in terms of f1-score and run time, enabling the images to be segmented in real time.