Abstract: Fully automated deep learning pipeline for adipose tissue segmentation on abdominal dixon MRI

Abstract

The accurate quantification of visceral and subcutaneous adipose tissue (VAT and SAT) has become a mayor interest worldwide, given that these tissue types represent an important risk factor of metabolic disorders. Currently, the gold standard for measuring volumes of VAT and SAT is the manual segmentation of abdominal fat images from 3D Dixon magnetic resonance (MR) scans – a very expensive and time-consuming process. To this end, we recently proposed Fat-SegNet [1] a fully automated pipeline to accurately segment adipose tissue inside a consistent anatomically defined abdominal region.