This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver
registration for image-pairs works by patch-wise prediction of a deformation model based …

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 …

Data augmentation is a key element in training high-dimensional models. In this approach,
one synthesizes new observations by applying pre-specified transformations to the original …

This paper presents DeepFLASH, a novel network with efficient training and inference for
learning-based medical image registration. In contrast to existing approaches that learn …

This paper introduces Fourier-approximated Lie algebras for shooting (FLASH), a fast
geodesic shooting algorithm for diffeomorphic image registration. We approximate the …

Spatial Transformer layers allow neural networks, at least in principle, to be invariant to large
spatial transformations in image data. The model has, however, seen limited uptake as most …

This paper presents an efficient algorithm for large deformation diffeomorphic metric
mapping (LDDMM) with geodesic shooting for image registration. We introduce a novel finite …

Time-series analysis is confounded by nonlinear time warping of the data. Traditional
methods for joint alignment do not generalize: after aligning a given signal ensemble, they …

Abstract 3D image registration is one of the most fundamental and computationally
expensive operations in medical image analysis. Here, we present a mixed-precision …

With this work we release CLAIRE, a distributed-memory implementation of an effective
solver for constrained large deformation diffeomorphic image registration problems in three …