  Childhood overweight and obesity is one of the main health problems in the
world since it is related to the early appearance of different diseases, in
addition to being a risk factor for later developing obesity in adulthood with
its health and economic consequences. Visceral abdominal tissue (VAT) is
strongly related to the development of metabolic and cardiovascular diseases
compared to abdominal subcutaneous adipose tissue (ASAT). Therefore, precise
and 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 and
automatic ASAT and VAT quantification methodologies have been proposed. In
particular, the semi-automated quantification methodology used commercially
through the cloud-based service AMRA R Researcher stands out due to its
extensive validation in different studies. In the present work, a database made
up 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 intensity
maps, a convolutional neural network (CNN) was proposed for the automatic
quantification of ASAT and VAT. The quantifications obtained from the proposed
methodology were compared with quantifications previously made through AMRA R
Researcher. For the comparison, correlation analysis, Bland-Altman graphs and
non-parametric statistical tests were used. The results indicated a high
correlation and similar precisions between the quantifications of this work and
those of AMRA R Researcher. The final objective is that the proposed
methodology can serve as an accessible and free tool for the diagnosis,
monitoring and prevention of diseases related to childhood obesity.
