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Abstract #4310

A semi-automatic method to segment visceral, subcutaneous and total fat in the abdomen from MRI data.

Caroline L. Hoad 1 , Kathryn Murray 1 , Jill Garratt 2 , Jan Smith 2 , David J. Humes 2 , Susan T. Francis 1 , Luca Marciani 2 , Robin C. Spiller 2 , and Penny A. Gowland 1

1 Sir Peter Mansfield Magnetic Resonance Centre, University of Nottingham, Nottingham, Nottinghamshire, United Kingdom, 2 Nottingham Digestive Diseases Centre, NIHR Biomedical Research Unit in GI and Liver Diseases, University Hospitals NHS Trust and the University of Nottingham, Nottingham, Nottinghamshire, United Kingdom

This study describes a semi-automatic segmentation algorithm to separate subcutaneous, visceral and total adipose fat from mDIXON MRI data. Data from 10 subjects with a wide range of BMIs were used to validate the algorithm. The algorithm used standard image processing techniques and did not use any training data. Excellent agreement between the algorithm and manual segmentation of the same data was found. Bland-Altman analysis found a small bias in the subcutaneous adipose tissue between manual and semi-automatic methods. Excellent agreement was also found between the results of 2 different observers.

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