Learning deformable image registration with structure guidance constraints for adaptive radiotherapy

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.