  Deformable image registration is fundamental for many medical image analyses.
A key obstacle for accurate image registration is the variations in image
appearance. Recently, deep learning-based registration methods (DLRs), using
deep neural networks, have computational efficiency that is several orders of
magnitude greater than traditional optimization-based registration methods
(ORs). A major drawback, however, of DLRs is a disregard for the
target-pair-specific optimization that is inherent in ORs and instead they rely
on a globally optimized network that is trained with a set of training samples
to achieve faster registration. Thus, DLRs inherently have degraded ability to
adapt to appearance variations and perform poorly, compared to ORs, when image
pairs (fixed/moving images) have large differences in appearance. Hence, we
propose an Appearance Adjustment Network (AAN) where we leverage anatomy edges,
through an anatomy-constrained loss function, to generate an anatomy-preserving
appearance transformation. We designed the AAN so that it can be readily
inserted into a wide range of DLRs, to reduce the appearance differences
between the fixed and moving images. Our AAN and DLR's network can be trained
cooperatively in an unsupervised and end-to-end manner. We evaluated our AAN
with two widely used DLRs - Voxelmorph (VM) and FAst IMage registration (FAIM)
- on three public 3D brain magnetic resonance (MR) image datasets - IBSR18,
Mindboggle101, and LPBA40. The results show that DLRs, using the AAN, improved
performance and achieved higher results than state-of-the-art ORs.
