Supervised learning aims to build a function or model that seeks as many map**s as
possible between the training data and outputs, where each training data will predict as a …

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 …

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 …

Radiological images such as computed tomography (CT) and X-rays render anatomy with
intrinsic structures. Being able to reliably locate the same anatomical structure across …

This article presents a generic probabilistic framework for estimating the statistical
dependency and finding the anatomical correspondences among an arbitrary number of …

Conventional medical image registration approaches directly optimize over the parameters
of a transformation model. These approaches have been highly successful and are used …

Learning-based registration for large-scale 3D point clouds has been shown to improve
robustness and accuracy compared to classical methods and can be trained without …

Unsupervised image registration commonly adopts U-Net style networks to predict dense
displacement fields in the full-resolution spatial domain. For high-resolution volumetric …

We present an approach to learning regular spatial transformations between image pairs in
the context of medical image registration. Contrary to optimization-based registration …