Automatic Quantification of Abdominal Subcutaneous and Visceral Adipose Tissue based on Dixon Sequences using Convolutional Neural Networks

Motivation: Currently there is a widely validated commercial semi-automatic method called AMRA® Researcher, which quantifies ASAT and VAT. However, it is not accessible to everyone due to the necessary economic means. Goal(s): To develop an automatic, simple and free methodology to quantify ASAT and VAT, with at least the same precision as AMRA® Researcher. Approach: Preprocessing and simple CNNs applied on in-phase Dixon MRI sequences were proposed for quantify VAT and ASAT. Results: There were no significant differences between the quantifications from AMRA Researcher and our methodology. Both obtained a high correlation and our methodology reached the precision of AMRA® Researcher. Impact: Our automatic, simple and free ASAT and VAT quantification methodology, studying MRI through preprocessing and CNNs, achieved the precision of the commercial semi-automatic AMRA Researcher method. After future independent validation, this could become an accessible tool to assist specialists.