Accurate segmentation of subcutaneous and intermuscular adipose tissue from MR images of the thigh

Abstract

Purpose: To describe and evaluate a computer-assisted method for assessing the quantity and distribution of adipose tissue in thigh by magnetic resonance imaging (MRI). Materials and Methods:Twenty obese subjects were imaged on a Philips Achieva 1.5T scanner by a fast spin-echo (FSE) sequence. A total of 636 images were acquired and analyzed by custom-made software. Thigh subcutaneous adipose tissue (SAT) and bone were identified by fuzzy clustering segmentation and an active contour algorithm. Muscle and intermuscular adipose tissue (IMAT) were assessed by identifying the two peaks of the signal histogram with an expectation maximization algorithm. The whole analysis was performed in an unsupervised manner without the need of any user interaction. Results:The coefficient of variation (CV) was evaluated between the unsupervised algorithm and manual analysis performed by an expert operator. The CV was low for all measurements (SAT Ͻ2%, muscle Ͻ1%, IMAT Ͻ5%). Limited manual correction of unsupervised segmentation results (less than 10% of contours modified) allowed us to further reduce the CV (SAT Ͻ0.5%, muscle Ͻ0.5%, IMAT Ͻ2%). Conclusion:The proposed approach allowed effective computer-assisted analysis of thigh MR images, dramatically reducing the user work compared to manual analysis. It allowed routine assessment of IMAT, a fat-depot linked with metabolic abnormalities, important in monitoring the effect of nutrition and exercise.

Test-retest reliability of rapid whole body and compartmental fat volume quantification on a widebore 3T MR system in normal-weight, overweight, and obese subjects

“…More recently introduced semi-automated and automated segmentation techniques have made this process faster (13)(14)(15)(16)(17)and there is increasing evidence of good reliability and correlation with the laborious standard of manual segmentation (18). Variances of up to 1% for semiautomated segmentation of superficial fat, and 1-2% for intramuscular adipose tissue (19) have been reported and test-retest reliability appears to be excellent with a study of visceral and subcutaneous adipose tissue in children using a fully automated process demonstrating correlation coefficients of r=0.977 for VAT and r=0.999 for subcutaneous adipose tissue (SAT) (20).…”

Localization And Quantification Of Intramuscular Damage Using Statistical Parametric Mapping And Skeletal Muscle Parcellation

“…The origin of the localization system was determined as the bone gravity center on the basis of an automatic segmentation of bone, muscle and subcutaneous adipose tissue. The automatic segmentation has been developed in C+ + using OpenCV library as previously published by Positano et al 50 in order to obtain a fast and fully automatic segmentation. While Positano et al used a gradient vector flow snake algorithm 51 , we used a more classical polygonal active contour algorithm 52 which determines the boundary between the subcutaneous fat and muscle tissues.…”

Adipose Measurement Using Micro MRI

“…In fuzzy logic, each pixel is assigned a fuzzy membership to indicate the probability that the pixel is fat. Then the fat is extracted by minimizing the membership function instead of being based solely on a threshold (Positano et al, 2004;Positano et al, 2009). More mathematically complex models have been created to compensate for the inhomogeneities in the intensity images (Hou, 2006;Vovk et al, 2007).…”

“…In order to separate the visceral and subcutaneous fat, curve deformation methods (Positano et al, 2004;Positano et al, 2009;Zhao et al, 2006) have also been reported. These methods deform a curve inwards from skin contour to locate a muscle layer.…”

“…In the previous research (Positano et al, 2004;Positano et al, 2009;Ranefall et al, 2009), the cluster number was usually defined to be three, corresponding to background, fat and muscles. Nevertheless, in the real anatomy, more organs and tissues are included in the MR image.…”