  Purpose: Development of a fast and fully automated deep learning pipeline
(FatSegNet) to accurately identify, segment, and quantify abdominal adipose
tissue on Dixon MRI from the Rhineland Study - a large prospective
population-based study. Method: FatSegNet is composed of three stages: (i)
consistent localization of the abdominal region using two 2D-Competitive Dense
Fully Convolutional Networks (CDFNet), (ii) segmentation of adipose tissue on
three views by independent CDFNets, and (iii) view aggregation. FatSegNet is
trained with 33 manually annotated subjects, and validated by: 1) comparison of
segmentation accuracy against a testingset covering a wide range of body mass
index (BMI), 2) test-retest reliability, and 3) robustness in a large cohort
study. Results: The CDFNet demonstrates increased robustness compared to
traditional deep learning networks. FatSegNet dice score outperforms manual
raters on the abdominal visceral adipose tissue (VAT, 0.828 vs. 0.788), and
produces comparable results on subcutaneous adipose tissue (SAT, 0.973 vs.
0.982). The pipeline has very small test-retest absolute percentage difference
and excellent agreement between scan sessions (VAT: APD = 2.957%, ICC=0.998 and
SAT: APD= 3.254%, ICC=0.996). Conclusion: FatSegNet can reliably analyze a 3D
Dixon MRI in1 min. It generalizes well to different body shapes, sensitively
replicates known VAT and SAT volume effects in a large cohort study, and
permits localized analysis of fat compartments.
