  Though, deep learning based medical image registration is currently starting
to show promising advances, often, it still fells behind conventional
frameworks in terms of registration accuracy. This is especially true for
applications where large deformations exist, such as registration of
interpatient abdominal MRI or inhale-to-exhale CT lung registration. Most
current works use U-Net-like architectures to predict dense displacement fields
from the input images in different supervised and unsupervised settings. We
believe that the U-Net architecture itself to some level limits the ability to
predict large deformations (even when using multilevel strategies) and
therefore propose a novel approach, where the input images are mapped into a
displacement space and final registrations are reconstructed from this
embedding. Experiments on inhale-to-exhale CT lung registration demonstrate the
ability of our architecture to predict large deformations in a single forward
path through our network (leading to errors below 2 mm).
