Abstract #4845

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

Benito de Celis Alonso¹, José Gerardo Suárez García², Po Wah-So³, Javier Miguel Hernández López¹, Silvia Sandra Hidalgo Tobón^4,5, and Pilar Dies Suárez⁶

¹Faculty of Physical and Mathematical Sciences, Benemérita Universidad Autónoma de Puebla, BUAP, Puebla, Mexico, ²Benemérita Universidad Autónoma de Puebla, BUAP, Puebla, Mexico, ³Department of Neuroimaging, Institute of Psychiatry, King´s College London, London, United Kingdom, ⁴Facultad de Ciencias, UAM Campus Iztapalapa, CDMX, Mexico, ⁵Imaging Department., Hospital Infantil de México, federico Gómez, CDMX, Mexico, ⁶Imaging Department, Hospital Infantil de México Federico Gómez, CDMX, Mexico

Synopsis

Keywords: AI/ML Software, Fat

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.

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