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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