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 …

Deformable image registration and regression are important tasks in medical image
analysis. However, they are computationally expensive, especially when analyzing large …

We propose a metamorphic geodesic regression approach approximating spatial
transformations for image time-series while simultaneously accounting for intensity changes …

Geodesic regression on images enables studies of brain development and degeneration,
disease progression, and tumor growth. The high-dimensional nature of image data …

Regression on medical image sequences can capture temporal image pattern changes and
predict images at missing or future time points. However, existing geodesic regression …

Longitudinal imaging allows for the study of structural changes over time. One approach to
detecting such changes is by non-linear image registration. This study introduces Multi …

In this thesis three diffeomorphic image registration methods are proposed. A Discretize-
then-Optimize approach is used to derive these methods from optimal control formulations in …

In this paper, we propose a predictive regression model for longitudinal images with missing
data based on large deformation diffeomorphic metric mapping (LDDMM) and deep neural …

Longitudinal brain image series offers the possibility to study individual brain anatomical
changes over time. Mathematical models are needed to study such developmental …

The human brain undergoes structural changes in size and in morphology between the
second and the third trimester of pregnancy, corresponding to accelerated growth and the …