• Christian Würslin, Jürgen Machann, Hansjörg Rempp, Claus Claussen, Bin Yang, and Fritz Schick.
• Department of Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany. christian.wuerslin@med.uni-tuebingen.de
• J Magn Reson Imaging. 2010 Feb 1; 31 (2): 430-9.

PurposeTo obtain quantitative measures of human body fat compartments from whole body MR datasets for the risk estimation in subjects prone to metabolic diseases without the need of any user interaction or expert knowledge.Materials And MethodsSets of axial T1-weighted spin-echo images of the whole body were acquired. The images were segmented using a modified fuzzy c-means algorithm. A separation of the body into anatomic regions along the body axis was performed to define regions with visceral adipose tissue present, and to standardize the results. In abdominal image slices, the adipose tissue compartments were divided into subcutaneous and visceral compartments using an extended snake algorithm. The slice-wise areas of different tissues were plotted along the slice position to obtain topographic fat tissue distributions.ResultsResults from automatic segmentation were compared with manual segmentation. Relatively low mean deviations were obtained for the class of total tissue (4.48%) and visceral adipose tissue (3.26%). The deviation of total adipose tissue was slightly higher (8.71%).ConclusionThe proposed algorithm enables the reliable and completely automatic creation of adipose tissue distribution profiles of the whole body from multislice MR datasets, reducing whole examination and analysis time to less than half an hour.

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