Deep learning technologies have dramatically reshaped the field of medical image
registration over the past decade. The initial developments, such as regression-based and U …

Medical image registration is a vital component of many medical procedures, such as image-
guided radiotherapy (IGRT), as it allows for more accurate dose-delivery and better …

In the last decade, convolutional neural networks (ConvNets) have been a major focus of
research in medical image analysis. However, the performances of ConvNets may be limited …

One of the fundamental challenges in supervised learning for multimodal image registration
is the lack of ground-truth for voxel-level spatial correspondence. This work describes a …

Deformable medical image registration has in past years been revolutionized by the use of
convolutional neural networks. These methods surpass conventional image registration …

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

A novel non-rigid image registration algorithm is built upon fully convolutional networks
(FCNs) to optimize and learn spatial transformations between pairs of images to be …

Purpose To develop an accurate and fast deformable image registration (DIR) method for
four‐dimensional computed tomography (4D‐CT) lung images. Deep learning‐based …

This work investigates the use of robust optimal transport (OT) for shape matching.
Specifically, we show that recent OT solvers improve both optimization-based and deep …

Deformable image registration is a very important field of research in medical imaging.
Recently multiple deep learning approaches were published in this area showing promising …