  Deformable image registration is a fundamental problem in the field of
medical image analysis. During the last years, we have witnessed the advent of
deep learning-based image registration methods which achieve state-of-the-art
performance, and drastically reduce the required computational time. However,
little work has been done regarding how can we encourage our models to produce
not only accurate, but also anatomically plausible results, which is still an
open question in the field. In this work, we argue that incorporating
anatomical priors in the form of global constraints into the learning process
of these models, will further improve their performance and boost the realism
of the warped images after registration. We learn global non-linear
representations of image anatomy using segmentation masks, and employ them to
constraint the registration process. The proposed AC-RegNet architecture is
evaluated in the context of chest X-ray image registration using three
different datasets, where the high anatomical variability makes the task
extremely challenging. Our experiments show that the proposed anatomically
constrained registration model produces more realistic and accurate results
than state-of-the-art methods, demonstrating the potential of this approach.
