  Purpose: An approach for the automated segmentation of visceral adipose
tissue (VAT) and subcutaneous adipose tissue (SAT) in multicenter water-fat MRI
scans of the abdomen was investigated, using two different neural network
architectures.
  Methods: The two fully convolutional network architectures U-Net and V-Net
were trained, evaluated and compared on the water-fat MRI data. Data of the
study Tellus with 90 scans from a single center was used for a 10-fold
cross-validation in which the most successful configuration for both networks
was determined. These configurations were then tested on 20 scans of the
multicenter study beta-cell function in JUvenile Diabetes and Obesity
(BetaJudo), which involved a different study population and scanning device.
  Results: The U-Net outperformed the used implementation of the V-Net in both
cross-validation and testing. In cross-validation, the U-Net reached average
dice scores of 0.988 (VAT) and 0.992 (SAT). The average of the absolute
quantification errors amount to 0.67% (VAT) and 0.39% (SAT). On the
multi-center test data, the U-Net performs only slightly worse, with average
dice scores of 0.970 (VAT) and 0.987 (SAT) and quantification errors of 2.80%
(VAT) and 1.65% (SAT).
  Conclusion: The segmentations generated by the U-Net allow for reliable
quantification and could therefore be viable for high-quality automated
measurements of VAT and SAT in large-scale studies with minimal need for human
intervention. The high performance on the multicenter test data furthermore
shows the robustness of this approach for data of different patient
demographics and imaging centers, as long as a consistent imaging protocol is
used.
