VoxelMorph: A Learning Framework for Deformable Medical Image Registration

@article{Balakrishnan2019VoxelMorphAL,
  title={VoxelMorph: A Learning Framework for Deformable Medical Image Registration},
  author={Guha Balakrishnan and Amy Zhao and Mert Rory Sabuncu and John V. Guttag and Adrian V. Dalca},
  journal={IEEE Transactions on Medical Imaging},
  year={2019},
  volume={38},
  pages={1788-1800}
}
We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical image registration. Traditional registration methods optimize an objective function for each pair of images, which can be time-consuming for large datasets or rich deformation models. In contrast to this approach and building on recent learning-based methods, we formulate registration as a function that maps an input image pair to a deformation field that aligns these images. We parameterize the function via… Expand
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References

SHOWING 1-10 OF 89 REFERENCES
An Unsupervised Learning Model for Deformable Medical Image Registration
TLDR
The proposed method uses a spatial transform layer to reconstruct one image from another while imposing smoothness constraints on the registration field, and demonstrates registration accuracy comparable to state-of-the-art 3D image registration, while operating orders of magnitude faster in practice. Expand
Robust Non-rigid Registration Through Agent-Based Action Learning
TLDR
This paper investigates in this paper how DL could help organ-specific (ROI-specific) deformable registration, to solve motion compensation or atlas-based segmentation problems for instance in prostate diagnosis and presents a training scheme with a large number of synthetically deformed image pairs requiring only a small number of real inter-subject pairs. Expand
Nonrigid Image Registration Using Multi-scale 3D Convolutional Neural Networks
TLDR
The proposed RegNet is trained using a large set of artificially generated DVFs, does not explicitly define a dissimilarity metric, and integrates image content at multiple scales to equip the network with contextual information, thereby greatly simplifying the training problem. Expand
Label-driven weakly-supervised learning for multimodal deformarle image registration
TLDR
A weakly-supervised, label-driven formulation for learning 3D voxel correspondence from higher-level label correspondence is proposed, thereby bypassing classical intensity-based image similarity measures. Expand
SVF-Net: Learning Deformable Image Registration Using Shape Matching
TLDR
An innovative approach for registration based on the deterministic prediction of the parameters from both images instead of the optimization of a energy criteria is proposed and shows an important improvement over a state of the art optimization based algorithm. Expand
End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network
TLDR
The results demonstrate that registration with DIRNet is as accurate as a conventional deformable image registration method with short execution times. Expand
Deformable Image Registration Based on Similarity-Steered CNN Regression
TLDR
A convolutional neural network (CNN) based regression model to directly learn the complex mapping from the input image pair to their corresponding deformation field, and it is found that the trained CNN model from one dataset can be successfully transferred to another dataset, although brain appearances across datasets are quite variable. Expand
Non-rigid image registration using fully convolutional networks with deep self-supervision
TLDR
A novel non-rigid image registration algorithm that is built upon fully convolutional networks (FCNs) to optimize and learn spatial transformations between pairs of images to be registered that has been evaluated for registering 3D structural brain magnetic resonance (MR) images and obtained better performance than state-of-the-art image registration algorithms. Expand
Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration
TLDR
This paper presents a probabilistic generative model and derive an unsupervised learning-based inference algorithm that makes use of recent developments in convolutional neural networks (CNNs) and results in state of the art accuracy and very fast runtimes, while providing diffeomorphic guarantees and uncertainty estimates. Expand
Quicksilver: Fast predictive image registration – A deep learning approach
TLDR
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. Expand
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1
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