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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