  The amounts of muscle and fat in a person's body, known as body composition,
are correlated with cancer risks, cancer survival, and cardiovascular risk. The
current gold standard for measuring body composition requires time-consuming
manual segmentation of CT images by an expert reader. In this work, we describe
a two-step process to fully automate the analysis of CT body composition using
a DenseNet to select the CT slice and U-Net to perform segmentation. We train
and test our methods on independent cohorts. Our results show Dice scores
(0.95-0.98) and correlation coefficients (R=0.99) that are favorable compared
to human readers. These results suggest that fully automated body composition
analysis is feasible, which could enable both clinical use and large-scale
population studies.
