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

• Published in: Journal of magnetic resonance imaging Vol. 29; no. 3; pp. 677 - 684
• Main Authors: Positano, Vincenzo, Christiansen, Tore, Santarelli, Maria Filomena, Ringgaard, Steffen, Landini, Luigi, Gastaldelli, Amalia
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.03.2009
• Subjects: adipose tissue; Adipose Tissue - pathology; Adult; Algorithms; fat; Female; fuzzy clustering; Humans; Image Processing, Computer-Assisted - methods; IMAT; Magnetic Resonance Imaging - methods; Male; MRI; Muscle, Skeletal - pathology; Obesity - pathology; Reproducibility of Results; SAT; Subcutaneous Fat - pathology; Thigh - pathology; Tissue Distribution
• ISSN: 1053-1807; 1522-2586
• DOI: 10.1002/jmri.21699

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 <2%, muscle <1%, IMAT <5%). Limited manual correction of unsupervised segmentation results (less than 10% of contours modified) allowed us to further reduce the CV (SAT <0.5%, muscle <0.5%, IMAT <2%). 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. J. Magn. Reson. Imaging 2009;29:677–684. © 2009 Wiley‐Liss, Inc.