, "Error estimation of deformable image registration of pulmonary CT scans using convolutional neural networks," J. Med. Imag. 5(2), 024003 (2018), doi: 10.1117/1.JMI.5.2.024003. 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 0.66 mm from ground truth landmark registration errors.
In this paper, we substantially extend the supervised deformable image registration approach we presented in REF , in which a convolutional neural network is used to perform fast image registration of 3D pulmonary CT images.
Error estimation of deformable image registration of pulmonary CT scans using convolutional neural networks
Journal of Medical Imaging
Engineering Computer Science Medicine