Accurate quantification of total body and the distribution of regional adipose tissue using manual segmentation is a challenging problem due to the high variation between manual delineations. Manual segmentation also requires highly trained experts with knowledge of anatomy. We present a hybrid segmentation method that provides robust delineation results for adipose tissue from whole body MRI scans. A formal evaluation of accuracy of the segmentation method is performed. This semi-automatic segmentation algorithm reduces significantly the time required for quantification of adipose tissue, and the accuracy measurements show that the results are close to the ground truth obtained from manual segmentations
Introduction: The present study aimed to suggest an unsupervised method for the segmentation of visc...
This paper presents an automatic method of correcting non-uniform RF coil response for the classific...
Automatic segmentation of adipose tissue in thigh magnetic resonance imaging (MRI) scans is challeng...
PURPOSE: To validate a fully automated adipose segmentation method with magnetic resonance imaging (...
An estimated one-third of the global population is now obese or overweight, a figure that has grown ...
Human brown adipose tissue (BAT), with a major site in the cervical-supraclavicular depot, is a prom...
Obesity currently affects 25% of Canadians and is strongly associated with many diseases including d...
Purpose: To describe and evaluate a computer-assisted method for assessing the quantity and distribu...
The MRI-based evaluation of the quantity and regional distribution of adipose tissue is one objectiv...
Fully automated and fast assessment of visceral and subcutaneous adipose tissue compartments using ...
The accurate determination of a person’s total body fat is an important issue in medical analysis be...
In this paper, we investigate the automatic detection of white and brown adipose tissues using Posit...
Background: Excess adipose tissue is associated with increased cardiovascular and metabolic risk, bu...
Abstract—We examine the technical challenges relating to the application of computer assisted diagno...
We developed a method for calculating adipose-tissue areas from transverse body scans by magnetic-re...
Introduction: The present study aimed to suggest an unsupervised method for the segmentation of visc...
This paper presents an automatic method of correcting non-uniform RF coil response for the classific...
Automatic segmentation of adipose tissue in thigh magnetic resonance imaging (MRI) scans is challeng...
PURPOSE: To validate a fully automated adipose segmentation method with magnetic resonance imaging (...
An estimated one-third of the global population is now obese or overweight, a figure that has grown ...
Human brown adipose tissue (BAT), with a major site in the cervical-supraclavicular depot, is a prom...
Obesity currently affects 25% of Canadians and is strongly associated with many diseases including d...
Purpose: To describe and evaluate a computer-assisted method for assessing the quantity and distribu...
The MRI-based evaluation of the quantity and regional distribution of adipose tissue is one objectiv...
Fully automated and fast assessment of visceral and subcutaneous adipose tissue compartments using ...
The accurate determination of a person’s total body fat is an important issue in medical analysis be...
In this paper, we investigate the automatic detection of white and brown adipose tissues using Posit...
Background: Excess adipose tissue is associated with increased cardiovascular and metabolic risk, bu...
Abstract—We examine the technical challenges relating to the application of computer assisted diagno...
We developed a method for calculating adipose-tissue areas from transverse body scans by magnetic-re...
Introduction: The present study aimed to suggest an unsupervised method for the segmentation of visc...
This paper presents an automatic method of correcting non-uniform RF coil response for the classific...
Automatic segmentation of adipose tissue in thigh magnetic resonance imaging (MRI) scans is challeng...