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 …

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 …

Voxelmorph: a learning framework for deformable medical image registration

G Balakrishnan, A Zhao, MR Sabuncu… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical
image registration. Traditional registration methods optimize an objective function for each …

Deep learning in medical image registration: a survey

G Haskins, U Kruger, P Yan - Machine Vision and Applications, 2020 - Springer
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 …

Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces

AV Dalca, G Balakrishnan, J Guttag, MR Sabuncu - Medical image analysis, 2019 - Elsevier
Classical deformable registration techniques achieve impressive results and offer a rigorous
theoretical treatment, but are computationally intensive since they solve an optimization …

An unsupervised learning model for deformable medical image registration

G Balakrishnan, A Zhao, MR Sabuncu… - Proceedings of the …, 2018 - openaccess.thecvf.com
We present a fast learning-based algorithm for deformable, pairwise 3D medical image
registration. Current registration methods optimize an objective function independently for …

Recursive cascaded networks for unsupervised medical image registration

S Zhao, Y Dong, EI Chang, Y Xu - Proceedings of the IEEE …, 2019 - openaccess.thecvf.com
We present recursive cascaded networks, a general architecture that enables learning deep
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

H Li, Y Fan - 2018 IEEE 15th International Symposium on …, 2018 - ieeexplore.ieee.org
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 …

SynthMorph: learning contrast-invariant registration without acquired images

M Hoffmann, B Billot, DN Greve… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
We introduce a strategy for learning image registration without acquired imaging data,
producing powerful networks agnostic to contrast introduced by magnetic resonance …

Networks for joint affine and non-parametric image registration

Z Shen, X Han, Z Xu… - Proceedings of the IEEE …, 2019 - openaccess.thecvf.com
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 …