  The establishment of image correspondence through robust image registration
is critical to many clinical tasks such as image fusion, organ atlas creation,
and tumor growth monitoring, and is a very challenging problem. Since the
beginning of the recent deep learning renaissance, the medical imaging research
community has developed deep learning based approaches and achieved the
state-of-the-art in many applications, including image registration. The rapid
adoption of deep learning for image registration applications over the past few
years necessitates a comprehensive summary and outlook, which is the main scope
of this survey. This requires placing a focus on the different research areas
as well as highlighting challenges that practitioners face. This survey,
therefore, outlines the evolution of deep learning based medical image
registration in the context of both research challenges and relevant
innovations in the past few years. Further, this survey highlights future
research directions to show how this field may be possibly moved forward to the
next level.
