Corpus ID: 49430783

AirLab: Autograd Image Registration Laboratory

@article{Sandkhler2018AirLabAI,
  title={AirLab: Autograd Image Registration Laboratory},
  author={Robin Sandk{\"u}hler and Christoph Jud and Simon Andermatt and Philippe C. Cattin},
  journal={ArXiv},
  year={2018},
  volume={abs/1806.09907}
}
Medical image registration is an active research topic and forms a basis for many medical image analysis tasks. Although image registration is a rather general concept specialized methods are usually required to target a specific registration problem. The development and implementation of such methods has been tough so far as the gradient of the objective has to be computed. Also, its evaluation has to be performed preferably on a GPU for larger images and for more complex transformation models… Expand
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References

SHOWING 1-10 OF 44 REFERENCES
elastix: A Toolbox for Intensity-Based Medical Image Registration
TLDR
The software consists of a collection of algorithms that are commonly used to solve medical image registration problems, and allows the user to quickly configure, test, and compare different registration methods for a specific application. Expand
Dense image registration through MRFs and efficient linear programming
TLDR
A novel and efficient approach to dense image registration, which does not require a derivative of the employed cost function is introduced, and efficient linear programming using the primal dual principles is considered to recover the lowest potential of the cost function. Expand
Learning Structured Deformations using Diffeomorphic Registration
TLDR
This work presents a registration approach which learns a low-dimensional stochastic parametrization of the deformation -- unsupervised, by looking at images, and constrain the deformations to be diffeomorphic using a new differentiable exponentiation layer. Expand
FAIR: Flexible Algorithms for Image Registration
TLDR
This book provides an overview of state-of-the-art registration techniques from theory to practice, plus numerous exercises designed to enhance readers understanding of the principles and mechanisms of the described techniques. Expand
Isotropic Total Variation Regularization of Displacements in Parametric Image Registration
TLDR
In isotropic Total Variation (TV) regularization is used to enable accurate registration near sliding interfaces in breathing motion databases and is robust to parameter selection, allowing the use of the same parameters for all tested databases. Expand
A survey of medical image registration - under review
TLDR
It may be concluded that the field of medical image registration has evolved, but still is in need of further development in various aspects. Expand
Nonrigid registration using free-form deformations: application to breast MR images
TLDR
The results clearly indicate that the proposed nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms. Expand
Multi-modal volume registration by maximization of mutual information
TLDR
A new information-theoretic approach is presented for finding the registration of volumetric medical images of differing modalities by adjustment of the relative position and orientation until the mutual information between the images is maximized. Expand
Multimodality image registration by maximization of mutual information
TLDR
The results demonstrate that subvoxel accuracy with respect to the stereotactic reference solution can be achieved completely automatically and without any prior segmentation, feature extraction, or other preprocessing steps which makes this method very well suited for clinical applications. Expand
Automated image registration: I. General methods and intrasubject, intramodality validation.
TLDR
The registration algorithm described is a robust and flexible tool that can be used to address a variety of image registration problems and can be tailored to meet different needs by optimizing tradeoffs between speed and accuracy. Expand
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