Given that image registration is a fundamental and ubiquitous task in both clinical and
research domains of the medical field, errors in registration can have serious consequences …

This paper introduces a novel method for inferring spatially varying regularisation in non-
linear registration. This is achieved through full Bayesian inference on a probabilistic …

The advent of large of multi-modal imaging databases opens up the opportunity to learn how
local intensity patterns covariate between multiple modalities. These models can then be …

In this paper we propose a new approach for spatially-varying registration using Gaussian
process priors. The method is based on the idea of spectral tempering, ie the spectrum of the …

This paper introduces a variational strategy to learn spatially-varying metrics on large
groups of images, in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) …

We extend Bayesian models of non-rigid image registration to allow not only for the
automatic determination of registration parameters (such as the trade-off between image …

This paper presents a novel mathematical framework for representing uncertainty in large
deformation diffeomorphic image registration. The Bayesian posterior distribution over the …

Recently inexpensive graphical processing units (GPUs) have become established as a
viable alternative to traditional CPUs for many medical image processing applications …

We present a technique to rectify nonrigid registrations by improving their group-wise
consistency, which is a widely used unsupervised measure to assess pair-wise registration …

This paper presents an efficient approach to quantifying image registration uncertainty
based on a low-dimensional representation of geometric deformations. In contrast to …