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

Koen A. J. Eppenhof; Josien P. W. Pluim

doi:10.1117/1.JMI.5.2.024003

10 May 2018 Error estimation of deformable image registration of pulmonary CT scans using convolutional neural networks

Koen A. J. Eppenhof, Josien P. W. Pluim

Author Affiliations +

Journal of Medical Imaging, Vol. 5, Issue 2, 024003 (May 2018). https://doi.org/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.

Citation Download Citation

Koen A. J. Eppenhof and Josien P. W. Pluim "Error estimation of deformable image registration of pulmonary CT scans using convolutional neural networks," Journal of Medical Imaging 5(2), 024003 (10 May 2018). https://doi.org/10.1117/1.JMI.5.2.024003

Received: 27 October 2017; Accepted: 23 April 2018; Published: 10 May 2018

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available