Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach
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Summary
This paper introduces Quicksilver, a fast deformable image registration method that accurately predicts registrations obtained by numerical optimization, is very fast, achieves state-of-the-art registration results on four standard validation datasets, and can jointly learn an image similarity measure.
- Type
- article
- Published
- 2017-03-31
- Cited by
- 645
- References
- 74
- Access
- Open access
- OpenAlex
- https://openalex.org/W28705497
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:14140372
Keywords
Tokamak, Nuclear engineering, Materials science, Fusion power, Power density
References
- A Hierarchical Geodesic Model for Diffeomorphic Longitudinal Shape Analysis
- Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference
- Finite-Dimensional Lie Algebras for Fast Diffeomorphic Image Registration
- FlowNet: Learning Optical Flow with Convolutional Networks
- Variational problems on ows of di eomorphisms for image matching
- Learning Based Non-rigid Multi-modal Image Registration Using Kullback-Leibler Divergence
- Semi-coupled dictionary learning for deformation prediction
- Numerical Methods for Image Registration
- Evaluating derivatives - principles and techniques of algorithmic differentiation, Second Edition
- Symmetric Atlasing and Model Based Segmentation: An Application to the Hippocampus in Older Adults
- Rectified Linear Units Improve Restricted Boltzmann Machines
- Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
- Longitudinal Brain MRI Analysis with Uncertain Registration
- Geodesic Regression for Image Time-Series
- Alignment by Maximization of Mutual Information
- Fully convolutional networks for semantic segmentation
- Optimal Control Formulation for Determining Optical Flow
- Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain
- Learning similarity measure for multi-modal 3D image registration
- Medical image registration: a review
Cited by
- Image Stacks as Parametric Surfaces: Application to Image Registration
- VoteNet: A Deep Learning Label Fusion Method for Multi-Atlas Segmentation
- Indirect Image Registration with Large Diffeomorphic Deformations
- Non-rigid image registration using fully convolutional networks with deep self-supervision
- To Learn or Not to Learn Features for Deformable Registration?
- Joint-Saliency Structure Adaptive Kernel Regression with Adaptive-Scale Kernels for Deformable Registration of Challenging Images
- Beyond Human-level License Plate Super-resolution with Progressive Vehicle Search and Domain Priori GAN
- Label-driven weakly-supervised learning for multimodal deformarle image registration
- Brain Extraction from Normal and Pathological Images: A Joint PCA/Image-Reconstruction Approach
- Deep Learning Applications in Medical Image Analysis
- Image Synthesis in Multi-Contrast MRI With Conditional Generative Adversarial Networks
- BIRNet: Brain image registration using dual-supervised fully convolutional networks
- An Unsupervised Learning Model for Deformable Medical Image Registration
- Real-time Deep Registration With Geodesic Loss
- Deformable Image Registration Using Cue-aware Deep Regression Network
- Temporal Interpolation via Motion Field Prediction
- Deep Diffeomorphic Transformer Networks
- Reducing Navigators in Free-Breathing Abdominal MRI via Temporal Interpolation Using Convolutional Neural Networks
- Stabilization and registration of full-motion video data using deep convolutional neural networks
- Error estimation of deformable image registration of pulmonary CT scans using convolutional neural networks
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