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J Magn Reson Imaging. 2013 Apr;37(4):917-27. doi: 10.1002/jmri.23884. Epub 2012 Oct 23.

Automated unsupervised multi-parametric classification of adipose tissue depots in skeletal muscle.

Author information

1
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA. alexander.valentinitsch@meduniwien.ac.at

Abstract

PURPOSE:

To introduce and validate an automated unsupervised multi-parametric method for segmentation of the subcutaneous fat and muscle regions to determine subcutaneous adipose tissue (SAT) and intermuscular adipose tissue (IMAT) areas based on data from a quantitative chemical shift-based water-fat separation approach.

MATERIALS AND METHODS:

Unsupervised standard k-means clustering was used to define sets of similar features (k = 2) within the whole multi-modal image after the water-fat separation. The automated image processing chain was composed of three primary stages: tissue, muscle, and bone region segmentation. The algorithm was applied on calf and thigh datasets to compute SAT and IMAT areas and was compared with a manual segmentation.

RESULTS:

The IMAT area using the automatic segmentation had excellent agreement with the IMAT area using the manual segmentation for all the cases in the thigh (R(2): 0.96) and for cases with up to moderate IMAT area in the calf (R(2): 0.92). The group with the highest grade of muscle fat infiltration in the calf had the highest error in the inner SAT contour calculation.

CONCLUSION:

The proposed multi-parametric segmentation approach combined with quantitative water-fat imaging provides an accurate and reliable method for an automated calculation of the SAT and IMAT areas reducing considerably the total postprocessing time.

PMID:
23097409
PMCID:
PMC3573225
DOI:
10.1002/jmri.23884
[Indexed for MEDLINE]
Free PMC Article
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