  In this paper, we propose a deep learning approach for image registration by
predicting deformation from image appearance. Since obtaining ground-truth
deformation fields for training can be challenging, we design a fully
convolutional network that is subject to dual-guidance: (1) Coarse guidance
using deformation fields obtained by an existing registration method; and (2)
Fine guidance using image similarity. The latter guidance helps avoid overly
relying on the supervision from the training deformation fields, which could be
inaccurate. For effective training, we further improve the deep convolutional
network with gap filling, hierarchical loss, and multi-source strategies.
Experiments on a variety of datasets show promising registration accuracy and
efficiency compared with state-of-the-art methods.
