Deep learning has received extensive research interest in developing new medical image
processing algorithms, and deep learning based models have been remarkably successful …

Multimodal image matching, which refers to identifying and then corresponding the same or
similar structure/content from two or more images that are of significant modalities or …

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 …

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 …

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 …

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 alignment of images through deformable image registration is vital to clinical
applications (eg, atlas creation, image fusion, and tumor targeting in image-guided …

This paper introduces an unsupervised adversarial similarity network for image registration.
Unlike existing deep learning registration methods, our approach can train a deformable …

Cardiac motion estimation and segmentation play important roles in quantitatively assessing
cardiac function and diagnosing cardiovascular diseases. In this paper, we propose a novel …

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 …