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 a generic Bayesian mixed-effects model to estimate the temporal progression of
a biological phenomenon from observations obtained at multiple time points for a group of …

We propose a Bayesian mixed-effects model to learn typical scenarios of changes from
longitudinal manifold-valued data, namely repeated measurements of the same objects or …

This paper explores the connections between traditional Large Deformation Diffeomorphic
Metric Mapping methods and unsupervised deep-learning approaches for non-rigid …

In this study we propose a deformation-based framework to jointly model the influence of
aging and Alzheimer's disease (AD) on the brain morphological evolution. Our approach …

We propose a method to learn a distribution of shape trajectories from longitudinal data, ie
the collection of individual objects repeatedly observed at multiple time-points. The method …

The analysis of longitudinal trajectories is a longstanding problem in medical imaging which
is often tackled in the context of Riemannian geometry: the set of observations is assumed to …

In non-parametric volumetric registration (NVR), the entire spatial transformation is treated
as an unknown function subject to a set of constraints, such as smoothness and invertibility …

Introduction Autopsy findings have shown the entorhinal cortex and transentorhinal cortex
are among the earliest sites of accumulation of pathology in patients developing Alzheimer's …

The infant brain undergoes a remarkable period of neural development that is crucial for the
development of cognitive and behavioral capacities (Hasegawa et al.,). Longitudinal …