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Childhood overweight and obesity is one of the main health problems in theworld since it is related to the early appearance of different diseases, inaddition to being a risk factor for later developing obesity in adulthood withits health and economic consequences. Visceral abdominal tissue (VAT) isstrongly related to the development of metabolic and cardiovascular diseasescompared to abdominal subcutaneous adipose tissue (ASAT). Therefore, preciseand automatic VAT and ASAT quantification methods would allow better diagnosis,monitoring and prevention of diseases caused by obesity at any stage of life.Currently, magnetic resonance imaging is the standard for fat quantification,with Dixon sequences being the most useful. Different semiautomatic andautomatic ASAT and VAT quantification methodologies have been proposed. Inparticular, the semi-automated quantification methodology used commerciallythrough the cloud-based service AMRA R Researcher stands out due to itsextensive validation in different studies. In the present work, a database madeup of Dixon MRI sequences, obtained from children between 7 and 9 years of age,was studied. Applying a preprocessing to obtain what we call total intensitymaps, a convolutional neural network (CNN) was proposed for the automaticquantification of ASAT and VAT. The quantifications obtained from the proposedmethodology were compared with quantifications previously made through AMRA RResearcher. For the comparison, correlation analysis, Bland-Altman graphs andnon-parametric statistical tests were used. The results indicated a highcorrelation and similar precisions between the quantifications of this work andthose of AMRA R Researcher. The final objective is that the proposedmethodology can serve as an accessible and free tool for the diagnosis,monitoring and prevention of diseases related to childhood obesity.

Automatic quantification of abdominal subcutaneous and visceral adipose  tissue in children, through MRI study, using total intensity maps and  Convolutional Neural Networks