  The UK Biobank Imaging Study has acquired medical scans of more than 40,000
volunteer participants. The resulting wealth of anatomical information has been
made available for research, together with extensive metadata including
measurements of liver fat. These values play an important role in metabolic
disease, but are only available for a minority of imaged subjects as their
collection requires the careful work of image analysts on dedicated liver MRI.
Another UK Biobank protocol is neck-to-knee body MRI for analysis of body
composition. The resulting volumes can also quantify fat fractions, even though
they were reconstructed with a two- instead of a three-point Dixon technique.
In this work, a novel framework for automated inference of liver fat from UK
Biobank neck-to-knee body MRI is proposed. A ResNet50 was trained for
regression on two-dimensional slices from these scans and the reference values
as target, without any need for ground truth segmentations. Once trained, it
performs fast, objective, and fully automated predictions that require no
manual intervention. On the given data, it closely emulates the reference
method, reaching a level of agreement comparable to different gold standard
techniques. The network learned to rectify non-linearities in the fat fraction
values and identified several outliers in the reference. It outperformed a
multi-atlas segmentation baseline and inferred new estimates for all imaged
subjects lacking reference values, expanding the total number of liver fat
measurements by factor six.
