bibtext: @InProceedings{ Sie_PULPo_MICCAI2024,
                   author = { Siegert, Leonard and Fischer, Paul and Heinrich, Mattias P. and Baumgartner, Christian F. },
                   title = { { PULPo: Probabilistic Unsupervised Laplacian Pyramid Registration } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15002 },
                   month = {October},
                   pages = { pending },
                }
abstract: Deformable image registration is fundamental to many medical imaging applications. Registration is an inherently ambiguous task often admitting many viable solutions. While neural network-based registration techniques enable fast and accurate registration, the majority of existing approaches are not able to estimate uncertainty. Here, we present PULPo, a method for probabilistic deformable registration capable of uncertainty quantification. PULPo probabilistically models the distribution of deformation fields on different hierarchical levels combining them using Laplacian pyramids. This allows our method to model global as well as local aspects of the deformation field. We evaluate our method on two widely used neuroimaging datasets and find that it achieves high registration performance as well as substantially better calibrated uncertainty quantification compared to the current state-of-the-art.
title: PULPo: Probabilistic Unsupervised Laplacian Pyramid Registration
authors: Siegert, Leonard Fischer, Paul Heinrich, Mattias P. Baumgartner, Christian F.