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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.19101 |
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| _version_ | 1866913707735384064 |
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| author | Henderson, Edward G. A. van Herk, Marcel Green, Andrew F. Osorio, Eliana M. Vasquez |
| author_facet | Henderson, Edward G. A. van Herk, Marcel Green, Andrew F. Osorio, Eliana M. Vasquez |
| contents | We propose an anatomically-informed initialisation method for interpatient CT non-rigid registration (NRR), using a learning-based model to estimate correspondences between organ structures. A thin plate spline (TPS) deformation, set up using the correspondence predictions, is used to initialise the scans before a second NRR step. We compare two established NRR methods for the second step: a B-spline iterative optimisation-based algorithm and a deep learning-based approach. Registration performance is evaluated with and without the initialisation by assessing the similarity of propagated structures. Our proposed initialisation improved the registration performance of the learning-based method to more closely match the traditional iterative algorithm, with the mean distance-to-agreement reduced by 1.8mm for structures included in the TPS and 0.6mm for structures not included, while maintaining a substantial speed advantage (5 vs. 72 seconds). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_19101 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An anatomically-informed correspondence initialisation method to improve learning-based registration for radiotherapy Henderson, Edward G. A. van Herk, Marcel Green, Andrew F. Osorio, Eliana M. Vasquez Computer Vision and Pattern Recognition We propose an anatomically-informed initialisation method for interpatient CT non-rigid registration (NRR), using a learning-based model to estimate correspondences between organ structures. A thin plate spline (TPS) deformation, set up using the correspondence predictions, is used to initialise the scans before a second NRR step. We compare two established NRR methods for the second step: a B-spline iterative optimisation-based algorithm and a deep learning-based approach. Registration performance is evaluated with and without the initialisation by assessing the similarity of propagated structures. Our proposed initialisation improved the registration performance of the learning-based method to more closely match the traditional iterative algorithm, with the mean distance-to-agreement reduced by 1.8mm for structures included in the TPS and 0.6mm for structures not included, while maintaining a substantial speed advantage (5 vs. 72 seconds). |
| title | An anatomically-informed correspondence initialisation method to improve learning-based registration for radiotherapy |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.19101 |