PULPo: Probabilistic Unsupervised Laplacian Pyramid Registration
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910527775571968 |
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| author | Siegert, Leonard Fischer, Paul Heinrich, Mattias P. Baumgartner, Christian F. |
| author_facet | Siegert, Leonard Fischer, Paul Heinrich, Mattias P. Baumgartner, Christian F. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_10567 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PULPo: Probabilistic Unsupervised Laplacian Pyramid Registration Siegert, Leonard Fischer, Paul Heinrich, Mattias P. Baumgartner, Christian F. Computer Vision and Pattern Recognition Image and Video Processing 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 |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2407.10567 |