  Image registration is a critical component in the applications of various
medical image analyses. In recent years, there has been a tremendous surge in
the development of deep learning (DL)-based medical image registration models.
This paper provides a comprehensive review of medical image registration.
Firstly, a discussion is provided for supervised registration categories, for
example, fully supervised, dual supervised, and weakly supervised registration.
Next, similarity-based as well as generative adversarial network (GAN)-based
registration are presented as part of unsupervised registration. Deep iterative
registration is then described with emphasis on deep similarity-based and
reinforcement learning-based registration. Moreover, the application areas of
medical image registration are reviewed. This review focuses on monomodal and
multimodal registration and associated imaging, for instance, X-ray, CT scan,
ultrasound, and MRI. The existing challenges are highlighted in this review,
where it is shown that a major challenge is the absence of a training dataset
with known transformations. Finally, a discussion is provided on the promising
future research areas in the field of DL-based medical image registration.
