Body composition imaging relies on assessment of tissues composition and distribution.
Quantitative data provided by body composition imaging analysis have been linked to …

Purpose To develop an automatic segmentation algorithm to classify abdominal adipose
tissues into visceral fat (VAT), deep (DSAT), and superficial (SSAT) subcutaneous fat …

The aim of the study was to create an accurate method of automated subcutaneous (SAT)
and visceral (VAT) adipose tissue detection basing on three-dimensional (3D) computed …

One major role of an accurate distribution of abdominal adipose tissue is to predict disease
risk. This paper proposes a novel effective three-level convolutional neural network (CNN) …

Objectives:(1) To develop a fully automated algorithm for segmentation of visceral adipose
tissue (VAT) and subcutaneous adipose tissue (SAT), excluding intermuscular adipose …

The present review summarizes the available evidence on artificial intelligence (AI)
algorithms aimed to the segmentation of epicardial and pericardial adipose tissues on …

Purpose To demonstrate that unsupervised assessment of abdominal adipose tissue
distribution by magnetic resonance imaging (MRI) can be improved by integrating automatic …

Purpose To describe and evaluate an automatic and unsupervised method for assessing the
quantity and distribution of abdominal adipose tissue by MRI. Material and Methods A total …

Background Segmentation of computed tomography (CT) images provides quantitative data
on body tissue composition, which may greatly impact the development and progression of …

Objective: To describe and evaluate a fully automated method for characterizing abdominal
adipose tissue from magnetic resonance (MR) transverse body scans. Methods: Four MR …