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Learning deformable image registration with structure guidance constraints for adaptive radiotherapy

: Kuckertz, S.; Papenberg, N.; Honegger, J.; Morgas, T.; Haas, B.; Heldmann, S.


Špiclin, Ž.:
Biomedical Image Registration. 9th International Workshop, WBIR 2020. Proceedings : Portorož, Slovenia, December 1-2, 2020
Cham: Springer Nature, 2020 (Lecture Notes in Computer Science 12120)
ISBN: 978-3-030-50119-8 (Print)
ISBN: 978-3-030-50120-4 (Online)
International Workshop on Biomedical Image Registration (WBIR) <9, 2020, Portoroz/Slovenia>
Fraunhofer MEVIS ()

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±0.08 compared to a score of 0.76±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±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.