A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
SK Zhou, H Greenspan, C Davatzikos… - Proceedings of the …, 2021 - ieeexplore.ieee.org
Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …
and has achieved remarkable success in many medical imaging applications, thereby …
Medical image registration using unsupervised deep neural network: A scoping literature review
S Abbasi, M Tavakoli, HR Boveiri, MAM Shirazi… - … Signal Processing and …, 2022 - Elsevier
In medicine, image registration is vital in image-guided interventions and other clinical
applications. However, it is a difficult subject to be addressed which by the advent of …
applications. However, it is a difficult subject to be addressed which by the advent of …
Voxelmorph: a learning framework for deformable medical image registration
We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical
image registration. Traditional registration methods optimize an objective function for each …
image registration. Traditional registration methods optimize an objective function for each …
Deep learning in medical image registration: a survey
The establishment of image correspondence through robust image registration is critical to
many clinical tasks such as image fusion, organ atlas creation, and tumor growth monitoring …
many clinical tasks such as image fusion, organ atlas creation, and tumor growth monitoring …
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 …
An unsupervised learning model for deformable medical image registration
We present a fast learning-based algorithm for deformable, pairwise 3D medical image
registration. Current registration methods optimize an objective function independently for …
registration. Current registration methods optimize an objective function independently for …
Recursive cascaded networks for unsupervised medical image registration
We present recursive cascaded networks, a general architecture that enables learning deep
cascades, for deformable image registration. The proposed architecture is simple in design …
cascades, for deformable image registration. The proposed architecture is simple in design …
Non-rigid image registration using self-supervised fully convolutional networks without training data
A novel non-rigid image registration algorithm is built upon fully convolutional networks
(FCNs) to optimize and learn spatial transformations between pairs of images to be …
(FCNs) to optimize and learn spatial transformations between pairs of images to be …
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 …
Networks for joint affine and non-parametric image registration
We introduce an end-to-end deep-learning framework for 3D medical image registration. In
contrast to existing approaches, our framework combines two registration methods: an affine …
contrast to existing approaches, our framework combines two registration methods: an affine …