A Deep Learning-based Fully Automated Approach for Body Composition Analysis in 3D Whole Body Dixon MRI

Abstract

An imbalanced ratio of body fat to muscle mass increases the risk of fatal illnesses, such as cardiovascular diseases, diabetes, and cancers. The analysis of this ratio requires the detection and segmentation of fat and muscle in medical images and precise segmentation of body composition helps to analyze more efficiently. Many existing commercial segmentation methods are either manual or semi-automatic, requiring intervention from reviewers or specialists. This paper introduces a fully automatic machine learning method that utilizes a 3D U-Net based Convolutional Neural Network to automatically segment muscles, adipose tissues and bones. To the best of our knowledge, this is the first application of a fully convolutional neural network(CNN) architecture for whole-body segmentation using magnetic resonance imaging (MRI). The 3D whole-body MRI scans were obtained using a 3D two-point Dixon VIBE sequence, which is the latest advancements for obtaining four types of MR images. The acquired data from 14 subjects fed into the U-Net model, which outputs the volumes of muscles, bone, and adipose tissues. The performance of the designed network was quantitatively evaluated using the Dice coefficient (Dice). The experimental results demonstrate that the proposed method is both simple and robust when compared to other studies on human. The average Dice on selected data reached 0.86.