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 …

This paper presents DeepFLASH, a novel network with efficient training and inference for
learning-based medical image registration. In contrast to existing approaches that learn …

Deformable templates are essential to large-scale medical image registration, segmentation,
and population analysis. Current conventional and deep network-based methods for …

Unsupervised image registration commonly adopts U-Net style networks to predict dense
displacement fields in the full-resolution spatial domain. For high-resolution volumetric …

U-Net style networks are commonly utilized in unsupervised image registration to predict
dense displacement fields, which for high-resolution volumetric image data is a resource …

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

Deformable shapes provide important and complex geometric features of objects presented
in images. However, such information is oftentimes missing or underutilized as implicit …

Subject motion is a long-standing problem of magnetic resonance imaging (MRI), which can
seriously deteriorate the image quality. Various prospective and retrospective methods have …

Deep learning-based image registration (DLIR) methods have achieved remarkable
success in deformable image registration. We observe that iterative inference can exploit the …

Time-series analysis is confounded by nonlinear time warping of the data. Traditional
methods for joint alignment do not generalize: after aligning a given signal ensemble, they …