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A survey on deep learning in medical image registration: New technologies, uncertainty, evaluation metrics, and beyond
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 …
registration over the past decade. The initial developments, such as regression-based and U …
Applications of artificial intelligence in cardiovascular imaging
Research into artificial intelligence (AI) has made tremendous progress over the past
decade. In particular, the AI-powered analysis of images and signals has reached human …
decade. In particular, the AI-powered analysis of images and signals has reached human …
Transmorph: Transformer for unsupervised medical image registration
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 …
research in medical image analysis. However, the performances of ConvNets may be limited …
Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces
Classical deformable registration techniques achieve impressive results and offer a rigorous
theoretical treatment, but are computationally intensive since they solve an optimization …
theoretical treatment, but are computationally intensive since they solve an optimization …
Correlation-aware coarse-to-fine mlps for deformable medical image registration
Deformable image registration is a fundamental step for medical image analysis. Recently
transformers have been used for registration and outperformed Convolutional Neural …
transformers have been used for registration and outperformed Convolutional Neural …
Unsupervised 3D end-to-end medical image registration with volume tweening network
3D medical image registration is of great clinical importance. However, supervised learning
methods require a large amount of accurately annotated corresponding control points (or …
methods require a large amount of accurately annotated corresponding control points (or …
H-vit: A hierarchical vision transformer for deformable image registration
This paper introduces a novel top-down representation approach for deformable image
registration which estimates the deformation field by capturing various short-and long-range …
registration which estimates the deformation field by capturing various short-and long-range …
SynthMorph: learning contrast-invariant registration without acquired images
We introduce a strategy for learning image registration without acquired imaging data,
producing powerful networks agnostic to contrast introduced by magnetic resonance …
producing powerful networks agnostic to contrast introduced by magnetic resonance …
BIRNet: Brain image registration using dual-supervised fully convolutional networks
In this paper, we propose a deep learning approach for image registration by predicting
deformation from image appearance. Since obtaining ground-truth deformation fields for …
deformation from image appearance. Since obtaining ground-truth deformation fields for …
Hypermorph: Amortized hyperparameter learning for image registration
We present HyperMorph, a learning-based strategy for deformable image registration that
removes the need to tune important registration hyperparameters during training. Classical …
removes the need to tune important registration hyperparameters during training. Classical …