This paper presents a review of deep learning (DL)-based medical image registration
methods. We summarized the latest developments and applications of DL-based registration …

Image-guided interventions are saving the lives of a large number of patients where the
image registration should indeed be considered as the most complex and complicated issue …

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 …

Classical deformable registration techniques achieve impressive results and offer a rigorous
theoretical treatment, but are computationally intensive since they solve an optimization …

3D medical image registration is of great clinical importance. However, supervised learning
methods require a large amount of accurately annotated corresponding control points (or …

We propose to learn a low-dimensional probabilistic deformation model from data which can
be used for the registration and the analysis of deformations. The latent variable model …

We develop a learning framework for building deformable templates, which play a
fundamental role in many image analysis and computational anatomy tasks. Conventional …

We propose a fully unsupervised multi-modal deformable image registration method
(UMDIR), which does not require any ground truth deformation fields or any aligned multi …

Deformable image registration is one of the fundamental tasks in medical imaging. Classical
registration algorithms usually require a high computational cost for iterative optimizations …

Nonlinear image registration continues to be a fundamentally important tool in medical
image analysis. Diagnostic tasks, image-guided surgery and radiotherapy as well as motion …