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


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).


CONCLUSIONS
The proposed automatic segmentation approach provides adequate thigh tissue segmentation and may be helpful in studies of regional composition.
IBFM-CNR, Palazzo LITA, Milan, Italy.|Department of Computer Science, University of Milano, Milan, Italy.
Sara
S
Orgiu
IBFM-CNR, Palazzo LITA, Milan, Italy.
Claudio L
CL
Lafortuna
IBFM-CNR, Palazzo LITA, Milan, Italy.
Fabio
F
Rastelli
Philips Healthcare, Monza, Italy.
Marcello
M
Cadioli
Department of Neuroradiology, Scientific Institute San Raffaele, Milan, Italy.
Andrea
A
Falini
IBFM-CNR, Palazzo LITA, Milan, Italy.
Giovanna
G
Rizzo
D000273:Adipose Tissue / Q000000981:diagnostic imaging*; D050154:Adiposity; D000328:Adult; D000367:Age Factors; D000368:Aged; D000465:Algorithms; D001823:Body Composition; D001330:Electronic Data Processing; D005205:Fascia / Q000000981:diagnostic imaging; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D008279:Magnetic Resonance Imaging; D008875:Middle Aged; D015233:Models, Statistical; D018482:Muscle, Skeletal / Q000000981:diagnostic imaging*; D009765:Obesity / Q000000981:diagnostic imaging / Q000503:physiopathology; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D013848:Thigh / Q000000981:diagnostic imaging*; D055815:Young Adult
IMAT; MRI; Snake; muscle; segmentation; thigh