The ability to compute body composition in cancer patients lends itself to determining the specific clinical
outcomes associated with fat and lean tissue stores. For example, a wasting syndrome of advanced disease
associates with shortened survival. Moreover, certain tissue compartments represent sites for drug distribution
and are likely determinants of chemotherapy efficacy and toxicity. CT images are abundant, but these cannot
be fully exploited unless there exist practical and fast approaches for tissue quantification. Here we propose a
fully automated method for segmenting muscle, visceral and subcutaneous adipose tissues, taking the approach
of shape modeling for the analysis of skeletal muscle. Muscle shape is represented using PCA encoded Free Form
Deformations with respect to a mean shape. The shape model is learned from manually segmented images and
used in conjunction with a tissue appearance prior. VAT and SAT are segmented based on the final deformed
muscle shape. In comparing the automatic and manual methods, coefficients of variation (COV) (1 - 2%), were
similar to or smaller than inter- and intra-observer COVs reported for manual segmentation.
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