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 …

The rapid advancements in machine learning, graphics processing technologies and the
availability of medical imaging data have led to a rapid increase in the use of deep learning …

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 …

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 …

Image registration is a fundamental task in multiple medical image analysis applications.
With the advent of deep learning, there have been significant advances in algorithmic …

Lung image registration can effectively describe the relative motion of lung tissues, thereby
helping to solve series problems in clinical applications. Since the lungs are soft and fairly …

In the last two years learning-based methods have started to show encouraging results in
different supervised and unsupervised medical image registration tasks. Deep neural …

Patient motion impacts single photon emission computed tomography (SPECT), positron
emission tomography (PET) and x-ray computed tomography (CT) by giving rise to …

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 …

Biomedical image analysis methods are gradually shifting towards computer-aided solutions
from manual investigations to save time and improve the quality of the diagnosis. Deep …