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 …

The alignment of images through deformable image registration is vital to clinical
applications (eg, atlas creation, image fusion, and tumor targeting in image-guided …

Classical diffeomorphic image registration methods, while being accurate, face the
challenges of high computational costs. Deep learning based approaches provide a fast …

Deformable image registration (DIR), aiming to find spatial correspondence between
images, is one of the most critical problems in the domain of medical image analysis. In this …

Diffeomorphic image registration, offering smooth transformation and topology preservation,
is required in many medical image analysis tasks. Traditional methods impose certain …

Image registration is an essential step in many medical image analysis tasks. Traditional
methods for image registration are primarily optimization-driven, finding the optimal …

Objective: Deformable Image Registration (DIR) plays a significant role in quantifying
deformation in medical data. Recent Deep Learning methods have shown promising …

Medical image analysis tasks are characterized by high-noise, volumetric, and multi-
modality, posing challenges for the model that attempts to learn robust features from the …

Deformable image registration (DIR) is crucial in medical image analysis, enabling the
exploration of biological dynamics such as organ motions and longitudinal changes in …

We propose a novel variational Bayesian formulation for diffeomorphic non-rigid registration
of medical images, which learns in an unsupervised way a data-specific similarity metric …