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Abstract #0751

Validation of Fully Automatic Adipose Tissue Segmentation & Volume Quantification

Bryan T. Addeman1, Melanie Beaton2, Robert A. Hegele3, Abraam S. Soliman3, 4, Curtis N. Wiens5, Charlies A. McKenzie1, 5

1Department of Medical Biophysics, University of Western Ontario, London, ON, Canada; 2Department of Medicine, University of Western Ontario, London, ON, Canada; 3The Robarts Research Institute, London, ON, Canada; 4Biomedical Engineering, University of Western Ontario, London, ON, Canada; 5Department of Physics, University of Western Ontario, London, ON, Canada

The distribution of adipose tissue is associated with the long-term development of type 2 diabetes and cardiovascular disease. Most adipose tissue volume quantification techniques require manual input, are susceptible to human error, and are time consuming. We propose a novel automated process for the quantification and segmentation of Total Adipose Tissue, Subcutaneous Adipose Tissue, and Intra-Abdominal Adipose Tissue using quantitative fat fraction maps. Segmentation is robust and requires no prior knowledge or complex machine learning. Results show that automated fat volume measurements are similar to manual segmentation techniques and can be calculated very rapidly.

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Keywords

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