Automated deep learning based segmentation of abdominal adipose tissue on whole-body MRI in a population-based study of adolescents

This study is embedded in the Generation R Study, a population-based prospective cohort study in the Netherlands. Whole-body Dixon MRI scans were performed at age 13 years. A previous neural network (CDFNet) has been published that was trained on adults. We aimed to retrain this network on our MRIs in the Generation R Study. The obtained segmentations are in strong agreement with expert-generated manual segmentations and can therefore greatly reduce the manual workload. Therefore, for accurate abdominal fat quantification, segmentation of both subcutaneous and visceral adipose tissue, on Dixon MRIs of adolescents is feasible with a retrained convolutional neural network.