  Purpose: To enable fast and reliable assessment of subcutaneous and visceral
adipose tissue compartments derived from whole-body MRI. Methods:
Quantification and localization of different adipose tissue compartments from
whole-body MR images is of high interest to examine metabolic conditions. For
correct identification and phenotyping of individuals at increased risk for
metabolic diseases, a reliable automatic segmentation of adipose tissue into
subcutaneous and visceral adipose tissue is required. In this work we propose a
3D convolutional neural network (DCNet) to provide a robust and objective
segmentation. In this retrospective study, we collected 1000 cases (66$\pm$ 13
years; 523 women) from the Tuebingen Family Study and from the German Center
for Diabetes research (TUEF/DZD), as well as 300 cases (53$\pm$ 11 years; 152
women) from the German National Cohort (NAKO) database for model training,
validation, and testing with a transfer learning between the cohorts. These
datasets had variable imaging sequences, imaging contrasts, receiver coil
arrangements, scanners and imaging field strengths. The proposed DCNet was
compared against a comparable 3D UNet segmentation in terms of sensitivity,
specificity, precision, accuracy, and Dice overlap. Results: Fast (5-7seconds)
and reliable adipose tissue segmentation can be obtained with high Dice overlap
(0.94), sensitivity (96.6%), specificity (95.1%), precision (92.1%) and
accuracy (98.4%) from 3D whole-body MR datasets (field of view coverage
450x450x2000mm${}^3$). Segmentation masks and adipose tissue profiles are
automatically reported back to the referring physician. Conclusion: Automatic
adipose tissue segmentation is feasible in 3D whole-body MR data sets and is
generalizable to different epidemiological cohort studies with the proposed
DCNet.
