Abstract #0667

Automated whole-body adipose tissue segmentation in T1-weighted fast spin echo imaging in a cohort of subjects at increased risk for type 2 diabetes

Thomas Kuestner^1,2,3, Martin Schwartz^2,3, Yipin Zhu³, Petros Martirosian², Bin Yang³, Sergios Gatidis², Jürgen Machann², and Fritz Schick²

¹King's College London, London, United Kingdom, ²Department of Radiology, University Hospital Tübingen, Tübingen, Germany, ³Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany

Quantification and localization of adipose tissues in whole-body T1-weighted MR images is of high interest to examine metabolic conditions. For correct identification and phenotyping of subjects at increased risk for metabolic diseases, reliable automatic segmentation of adipose tissue into subcutaneous adipose tissue and visceral adipose tissue is required. Full manual tissue delineation is a time-and cost-intensive task which is not advisable especially in cohort studies. We propose a 3D convolutional neural network to perform automated adipose tissue segmentation from T1-weighted whole-body fast spin echo images in a fast and robust way with reliable separation of visceral and subcutaneous fat masses.

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