Epicardial Adipose Tissue Segmentation from CT Images with A Semi-3D Neural Network

TL;DR

This paper introduces a semi-3D neural network approach for fully automatic segmentation of epicardial adipose tissue from CT images, aiding cardiovascular risk assessment.

Contribution

It presents a U-Net-based deep learning model that incorporates slice depth information for improved segmentation accuracy.

Findings

Achieved a Dice score of 0.86 on patient CT scans.

Demonstrated robustness through image augmentation.

Enabled automated large-scale epicardial tissue analysis.

Abstract

Epicardial adipose tissue is a type of adipose tissue located between the heart wall and a protective layer around the heart called the pericardium. The volume and thickness of epicardial adipose tissue are linked to various cardiovascular diseases. It is shown to be an independent cardiovascular disease risk factor. Fully automatic and reliable measurements of epicardial adipose tissue from CT scans could provide better disease risk assessment and enable the processing of large CT image data sets for a systemic epicardial adipose tissue study. This paper proposes a method for fully automatic semantic segmentation of epicardial adipose tissue from CT images using a deep neural network. The proposed network uses a U-Net-based architecture with slice depth information embedded in the input image to segment a pericardium region of interest, which is used to obtain an epicardial adipose tissue segmentation. Image augmentation is used to increase model robustness. Cross-validation of the proposed method yields a Dice score of 0.86 on the CT scans of 20 patients.