Error estimation of deformable image registration of pulmonary CT scans using convolutional neural networks

Journal of Medical Imaging
Koen A J Eppenhof, Josien P W Pluim

Abstract

Error estimation in nonlinear medical image registration is a nontrivial problem that is important for validation of registration methods. We propose a supervised method for estimation of registration errors in nonlinear registration of three-dimensional (3-D) images. The method is based on a 3-D convolutional neural network that learns to estimate registration errors from a pair of image patches. By applying the network to patches centered around every voxel, we construct registration error maps. The network is trained using a set of representative images that have been synthetically transformed to construct a set of image pairs with known deformations. The method is evaluated on deformable registrations of inhale-exhale pairs of thoracic CT scans. Using ground truth target registration errors on manually annotated landmarks, we evaluate the method's ability to estimate local registration errors. Estimation of full domain error maps is evaluated using a gold standard approach. The two evaluation approaches show that we can train the network to robustly estimate registration errors in a predetermined range, with subvoxel accuracy. We achieved a root-mean-square deviation of 0.51 mm from gold standard registration errors and of ...Continue Reading

Citations

Jul 14, 2020·Journal of Applied Clinical Medical Physics·Yi GuoX George Xu
Mar 29, 2020·Physics in Medicine and Biology·Yabo FuXiaofeng Yang
Sep 10, 2020·Physics in Medicine and Biology·Baudouin Denis de SennevillePierrick Coupé
Jun 6, 2021·Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society·Yi LuoZhihai He
Jul 4, 2021·Medical Image Analysis·Alessa HeringBram van Ginneken
Sep 16, 2021·Medical Physics·Maarten L TerpstraCornelis A T van den Berg

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