In the last decade, convolutional neural networks (ConvNets) have been a major focus of
research in medical image analysis. However, the performances of ConvNets may be limited …

Over the past decade, deep learning technologies have greatly advanced the field of
medical image registration. The initial developments, such as ResNet-based and U-Net …

Complicated deformation problems are frequently encountered in medical image
registration tasks. Although various advanced registration models have been proposed …

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

Medical images are often characterized by their structured anatomical representations and
spatially inhomogeneous contrasts. Leveraging anatomical priors in neural networks can …

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

Deep Learning in Image Registration (DLIR) methods have been tremendously successful
in image registration due to their speed and ability to incorporate weak label supervision at …

Understanding the uncertainty inherent in deep learning-based image registration models
has been an ongoing area of research. Existing methods have been developed to quantify …

Deformable image registration plays a crucial role in estimating cardiac deformation from a
sequence of images. However, existing registration methods primarily process images as …

Background Deep learning‐based methods have been successfully applied to MRI image
registration. However, there is a lack of deep learning‐based registration methods for …