FatSegNet: A fully automated deep learning pipeline for adipose tissue segmentation on abdominal dixon MRI.

Santiago EstradaRan LuSailesh ConjetiXimena Orozco-RuizJoana Panos-WilluhnMonique M B Breteler Martin Reuter

Published in: Magnetic resonance in medicine (2019)

FatSegNet generalizes well to different body shapes, sensitively replicates known VAT and SAT volume effects in a large cohort study and permits localized analysis of fat compartments. Furthermore, it can reliably analyze a 3D Dixon MRI in ∼1 minute, providing an efficient and validated pipeline for abdominal adipose tissue analysis in the Rhineland Study.

Keyphrases

adipose tissue
deep learning
contrast enhanced
magnetic resonance imaging
insulin resistance
convolutional neural network
high fat diet
artificial intelligence
machine learning
diffusion weighted imaging
type diabetes
computed tomography
single cell