Abstract
Accurate registration of CT and CBCT images is key for adaptive radiotherapy. A particular challenge is the alignment of flexible organs, such as bladder or rectum, that often yield extreme deformations. In this work we analyze the impact of so-called structure guidance for learning based registration when additional segmentation information is provided to a neural network. We present a novel weakly supervised deep learning based method for multi-modal 3D deformable CT-CBCT registration with structure guidance constraints. Our method is not supervised by ground-truth deformations and we use the energy functional of a variational registration approach as loss for training. Incorporating structure guidance constraints in our learning based approach results in an average Dice score of $$0.91\pm 0.08$$ compared to a score of $$0.76\pm 0.15$$ for the same method without constraints. An iterative registration approach with structure guidance results in a comparable average Dice score of $$0.91\pm 0.09$$. However, learning based registration requires only a single pass through the network, yielding computation of a deformation fields in less than 0.1 s which is more than 100 times faster than the runtime of iterative registration.
Original language | English |
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Title of host publication | WBIR 2020: Biomedical Image Registration |
Editors | Žiga Špiclin, Jamie McClelland, Jan Kybic, Orcun Goksel |
Number of pages | 10 |
Volume | 12120 LNCS |
Publisher | Springer, Cham |
Publication date | 09.06.2020 |
Pages | 44-53 |
ISBN (Print) | 978-3-030-50119-8 |
ISBN (Electronic) | 978-3-030-50120-4 |
DOIs | |
Publication status | Published - 09.06.2020 |
Event | 9th International Workshop on Biomedical Image Registration - Portoroz, Slovenia Duration: 01.12.2020 → 02.12.2020 Conference number: 240939 |