Automatic muscle and fat segmentation in the thigh from T1-Weighted MRI

• Published in: Journal of magnetic resonance imaging Vol. 43; no. 3; pp. 601 - 610
• Main Authors: Orgiu, Sara, Lafortuna, Claudio L., Rastelli, Fabio, Cadioli, Marcello, Falini, Andrea, Rizzo, Giovanna
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.03.2016; Wiley Subscription Services, Inc
• Subjects: Adipose Tissue - diagnostic imaging; Adiposity; Adult; Age Factors; Aged; Algorithms; Automatic Data Processing; Body Composition; Fascia - diagnostic imaging; Female; Fuzzy Logic; Humans; IMAT; Magnetic Resonance Imaging; Middle Aged; Models, Statistical; MRI; muscle; Muscle, Skeletal - diagnostic imaging; Obesity - diagnostic imaging; Obesity - physiopathology; Pattern Recognition, Automated; Reproducibility of Results; segmentation; Snake; thigh; Thigh - diagnostic imaging; Young Adult; segmentation; IMAT; MRI; Snake; thigh; muscle
• ISSN: 1053-1807; 1522-2586
• DOI: 10.1002/jmri.25031

Purpose To introduce and validate an automatic segmentation method for the discrimination of skeletal muscle (SM), and adipose tissue (AT) components (subcutaneous adipose tissue [SAT] and intermuscular adipose tissue [IMAT]) from T1‐weighted (T1‐W) magnetic resonance imaging (MRI) images of the thigh. Materials and Methods Eighteen subjects underwent an MRI examination on a 1.5T Philips Achieva scanner. Acquisition was performed using a T1‐W sequence (TR = 550 msec, TE = 15 msec), pixel size between 0.81–1.28 mm, slice thickness of 6 mm. Bone, AT, and SM were discriminated using a fuzzy c‐mean algorithm and morphologic operators. The muscle fascia that separates SAT from IMAT was detected by integrating a morphological‐based segmentation with an active contour Snake. The method was validated on five young normal weight, five older normal weight, and five older obese females, comparing automatic with manual segmentations. Results We reported good performance in the extraction of SM, AT, and bone in each subject typology (mean sensitivity above 96%, mean relative area difference of 1.8%, 2.7%, and 2.5%, respectively). A mean distance between contours pairs of 0.81 mm and a mean percentage of contour points with distance smaller than 2 pixels of 86.2% were obtained in the muscle fascia identification. Significant correlation was also found between manual and automatic IMAT and SAT cross‐sectional areas in all subject typologies (p < 0.001). Conclusion The proposed automatic segmentation approach provides adequate thigh tissue segmentation and may be helpful in studies of regional composition. J. MAGN. RESON. IMAGING 2016;43:601–610.