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Bayesian Estimation of Regularization and Atlas Building in Diffeomorphic Image Registration
TLDR
An atlas estimation procedure that simultaneously estimates the parameters controlling the smoothness of the diffeomorphic transformations and a Monte Carlo Expectation Maximization algorithm, where the expectation step is approximated via Hamiltonian Monte Carlo sampling on the manifold of diffeomorphisms.
Probabilistic Principal Geodesic Analysis
TLDR
This work presents a latent variable model for PGA that provides a probabilistic framework for factor analysis on manifolds, and develops a Monte Carlo Expectation Maximization algorithm, where the expectation is approximated by Hamiltonian Monte Carlo sampling of the latent variables.
Fast Diffeomorphic Image Registration via Fourier-Approximated Lie Algebras
TLDR
This paper introduces Fourier-approximated Lie algebras for shooting (FLASH), a fast geodesic shooting algorithm for diffeomorphic image registration that approximate the infinite-dimensional Lie algebra of smooth vector fields with a low-dimensional, bandlimited space.
DeepFLASH: An Efficient Network for Learning-Based Medical Image Registration
TLDR
Experimental results show that the DeepFLASH method is significantly faster than the state-of-the-art deep learning based image registration methods, while producing equally accurate alignment.
Bayesian Principal Geodesic Analysis in Diffeomorphic Image Registration
TLDR
A generative Bayesian approach for automatic dimensionality reduction of shape variability represented through diffeomorphic mappings is presented, and a latent variable model for principal geodesic analysis (PGA) that provides a probabilistic framework for factor analysis on diffeomorphisms is developed.
Finite-Dimensional Lie Algebras for Fast Diffeomorphic Image Registration
TLDR
A novel finite-dimensional Lie algebra structure on the space of bandlimited velocity fields is introduced that can effectively represent initial velocities for diffeomorphic image registration at much lower dimensions than typically used, with little to no loss in registration accuracy.
Temporal Registration in In-Utero Volumetric MRI Time Series
TLDR
The proposed model captures accurately the temporal dynamics of deformations in in-utero MRI time series and demonstrates the utility of the temporal model by showing that its use improves the accuracy of the segmentation propagation through temporal registration.
On the Applicability of Registration Uncertainty
TLDR
It is shown that there are two types of uncertainties in registration uncertainty: the transformation uncertainty, \(U_\mathrm {t}\), and label uncertainty, and it is shared a potentially critical finding that making use of the registration uncertainty may not always be an improvement.
Frequency Diffeomorphisms for Efficient Image Registration
TLDR
A novel finite dimensional Fourier representation of diffeomorphic deformations based on the key fact that the high frequency components of a diffeomorphism remain stationary throughout the integration process when computing the deformation associated with smooth velocity fields is introduced.
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