  We propose a supervised nonrigid image registration method, trained using
artificial displacement vector fields (DVF), for which we propose and compare
three network architectures. The artificial DVFs allow training in a fully
supervised and voxel-wise dense manner, but without the cost usually associated
with the creation of densely labeled data. We propose a scheme to artificially
generate DVFs, and for chest CT registration augment these with simulated
respiratory motion. The proposed architectures are embedded in a multi-stage
approach, to increase the capture range of the proposed networks in order to
more accurately predict larger displacements. The proposed method, RegNet, is
evaluated on multiple databases of chest CT scans and achieved a target
registration error of 2.32 $\pm$ 5.33 mm and 1.86 $\pm$ 2.12 mm on SPREAD and
DIR-Lab-4DCT studies, respectively. The average inference time of RegNet with
two stages is about 2.2 s.
