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).
View on arXiv@article{henderson2025_2502.19101,
title={ An anatomically-informed correspondence initialisation method to improve learning-based registration for radiotherapy },
author={ Edward G. A. Henderson and Marcel van Herk and Andrew F. Green and Eliana M. Vasquez Osorio },
journal={arXiv preprint arXiv:2502.19101},
year={ 2025 }
}