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 …

Motivated by the rapid and current progress to develop intelligent image-guided intervention
tools, we aim in this paper to present, a recent review of a specific family of deep learning …

Background and objective Non-rigid image registration plays a significant role in computer-
aided diagnosis and surgical navigation for brain diseases. Registration methods that utilize …

Transformers have recently shown promise for medical image applications, leading to an
increasing interest in develo** such models for medical image registration. Recent …

Purpose Deformable image registration establishes non-linear spatial correspondences
between fixed and moving images. Deep learning–based deformable registration methods …

VoxelMorph, proposed in 2018, utilizes Convolutional Neural Networks (CNNs) to address
medical image registration problems. In 2021 TransMorph advanced this approach by …

In the past, optimization-based registration models have used spatially-varying
regularization to account for deformation variations in different image regions. However …

Spatially varying regularization accommodates the deformation variations that may be
necessary for different anatomical regions during deformable image registration. Historically …

Predicting future brain states is crucial for understanding healthy aging and
neurodegenerative diseases. Longitudinal brain MRI registration, a cornerstone for such …

Deformable image registration is inherently a multi-objective optimization (MOO) problem,
requiring a delicate balance between image similarity and deformation regularity. These …