Abstract
Ideally the deformation field from one image to another should be invertible and smooth to register images bidirectionally and preserve topology of anatomical structures. In traditional registration methods, differential geometry constraints could guarantee such topological consistency but are computationally intensive and time consuming. Recent studies showed that image registration using deep neural networks is as accurate as and also much faster than traditional methods. Current popular unsupervised learning-based algorithms aim to directly estimate spatial transformations by optimizing similarity between images under registration; however, the estimated deformation fields are often in one direction and do not possess inverse-consistency if swapping the order of two input images. Notice that the consistent registration can reduce systematic bias caused by the order of input images, increase robustness, and improve reliability of subsequent data analysis. Accordingly, in this paper, we propose a new training strategy by introducing both pair-wise and group-wise deformation consistency constraints. Specifically, losses enforcing both inverse-consistency for image pairs and cycle-consistency for image groups are proposed for model training, in addition to conventional image similarity and topology constraints. Experiments on 3D brain magnetic resonance (MR) images showed that such a learning algorithm yielded consistent deformations even after switching the order of input images or reordering images within groups. Furthermore, the registration results of longitudinal elderly MR images demonstrated smaller volumetric measurement variability in labeling regions of interest (ROIs).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Courchesne, E., et al.: Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers. Radiology 216(3), 672–682 (2000)
Jack, C.R., et al.: Rates of hippocampal atrophy correlate with change in clinical status in aging and AD. Neurology 55(4), 484–490 (2000)
Hart, G.L., Zach, C., Niethammer, M.: An optimal control approach for deformable registration. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9–16 (2009)
Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26–41 (2008)
Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic demons: efficient non-parametric image registration. NeuroImage 45(1), S61–S72 (2009)
Cao, X., Fan, J., Dong, P., Ahmad, S., Yap, P.-T., Shen, D.: Image registration using machine and deep learning. In: Handbook of Medical Image Computing and Computer-Assisted Intervention, pp. 319–342. Academic Press (2020)
Haskins, G., Kruger, U., Yan, P.: Deep learning in medical image registration: a survey. arXiv preprint arXiv:1903.02026 (2019)
Fu, Y., Lei, Y., Wang, T., Curran, W.J., Liu, T., Yang, X.: Deep learning in medical image registration: a review. arXiv preprint arXiv:1912.12318 (2019)
Dalca, A.V., Balakrishnan, G., Guttag, J., Sabuncu, M.R.: Unsupervised learning for fast probabilistic diffeomorphic registration. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 729–738. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_82
Kim, B., Kim, J., Lee, J.-G., Kim, D.H., Park, S.H., Ye, J.C.: Unsupervised deformable image registration using cycle-consistent CNN. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 166–174. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_19
Zhang, J.: Inverse-consistent deep networks for unsupervised deformable image registration. arXiv preprint arXiv:1809.03443 (2018)
Kuang, D.: Cycle-consistent training for reducing negative jacobian determinant in deep registration networks. In: Burgos, N., Gooya, A., Svoboda, D. (eds.) SASHIMI 2019. LNCS, vol. 11827, pp. 120–129. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32778-1_13
Mueller, S.G., Weiner, M.W., Thal, L.J., Petersen, R.C.: Ways toward an early diagnosis in Alzheimer’s disease: The Alzheimer’s Disease Neuroimaging Initiative (ADNI). Alzheimer’s Dement. 1(1), 55–66 (2005)
Klein, A., et al.: Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage 46(3), 786–802 (2009)
Fan, J., Cao, X., Xue, Z., Yap, P.-T., Shen, D.: BIRNet: brain image registration using dual-supervised fully convolutional networks. Med. Image Anal. 54, 193–206 (2019)
Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: VoxelMorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imaging 38(8), 1788–1800 (2019)
Acknowledgement
This work was partially supported by the National Key Research and Development Program of China (2018YFC0116400) and the National Natural Science Foundation of China (61671204).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Gu, D. et al. (2020). Pair-Wise and Group-Wise Deformation Consistency in Deep Registration Network. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12263. Springer, Cham. https://doi.org/10.1007/978-3-030-59716-0_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-59716-0_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59715-3
Online ISBN: 978-3-030-59716-0
eBook Packages: Computer ScienceComputer Science (R0)
