Rationale and Objectives The automated segmentation of organs and tissues throughout the
body using computed tomography and magnetic resonance imaging has been rapidly …

Rapid technological advances in non-invasive imaging, coupled with the availability of large
data sets and the expansion of computational models and power, have revolutionized the …

Epicardial adipose tissue (EAT) is a visceral fat deposit related to coronary artery disease.
Fully automated quantification of EAT volume in clinical routine could be a timesaving and …

Purpose To evaluate the performance of deep learning for robust and fully automated
quantification of epicardial adipose tissue (EAT) from multicenter cardiac CT data. Materials …

Cardiovascular imaging technologies continue to increase in their capacity to capture and
store large quantities of data. Modern computational methods, developed in the field of …

Epicardial adipose tissue (EAT) is located between the myocardium and visceral
pericardium. The unique anatomy and physiology of the EAT determines its great potential …

Many studies have shown that epicardial fat is associated with a higher risk of heart
diseases. Accurate epicardial adipose tissue quantification is still an open research issue …

Background Analytic morphomics, or more simply,“morphomics,” refers to the measurement
of specific biomarkers of body composition from medical imaging, most commonly computed …

Background Artificial intelligence (AI) technology has been increasingly developed and
studied in cardiac imaging. This systematic review summarizes the latest progress of image …

Epicardial adipose tissue (EAT) significantly contributes to the progression of cardiovascular
diseases (CVDs). However, manually quantifying EAT volume is labor-intensive and …