Quantifying Registration Uncertainty With Sparse Bayesian Modelling
Quantifying Registration Uncertainty With Sparse Bayesian Modelling
We investigate uncertainty quantification under a sparse Bayesian model of medical image registration. Bayesian modelling has proven powerful to automate the tuning of registration hyperparameters, such as the trade-off between the data and regularization functionals. Sparsity-inducing priors have recently been used to render the parametrization itself adaptive and data-driven. The …