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Proceedings Paper

Automated segmentation of muscle and adipose tissue on CT images for human body composition analysis
Author(s): Howard Chung; Dana Cobzas; Laura Birdsell; Jessica Lieffers; Vickie Baracos
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Paper Abstract

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.

Paper Details

Date Published: 13 March 2009
PDF: 8 pages
Proc. SPIE 7261, Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling, 72610K (13 March 2009); doi: 10.1117/12.812412
Show Author Affiliations
Howard Chung, Univ. of Alberta (Canada)
Dana Cobzas, Univ. of Alberta (Canada)
Laura Birdsell, Univ. of Alberta (Canada)
Jessica Lieffers, Univ. of Alberta (Canada)
Vickie Baracos, Univ. of Alberta (Canada)

Published in SPIE Proceedings Vol. 7261:
Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling
Michael I. Miga; Kenneth H. Wong, Editor(s)

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