Deformable registration, or nonlinear alignment of images, is a fundamental preprocessing tool in medical imaging. State-of-the-art algorithms restrict to diffeomorphisms to regularize an otherwise ill-posed problem. In particular, such models assume that a one-to-one matching exists between any pair of images. In a range of real-life-applications, however, one image may contain objects that another does not. In such cases, the one-to-one assumption is routinely accepted as unavoidable, leading to inaccurate preprocessing and, thus, inaccuracies in the subsequent analysis. We present a novel, piecewise-diffeomorphic deformation framework which models topological changes as explicitly encoded discontinuities in the deformation fields. We thus preserve the regularization properties of diffeomorphic models while locally avoiding their erroneous one-to-one assumption. The entire model is GPU-implemented, and validated on intersubject 3D registration of T1-weighted brain MRI. Qualitative and quantitative results show our ability to improve performance in pathological cases containing topological inconsistencies.
- Image registration
This is a preview of subscription content, access via your institution.
Tax calculation will be finalised at checkout
Purchases are for personal use onlyLearn about institutional subscriptions
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)
Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: An unsupervised learning model for deformable medical image registration. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9252–9260(2018)
Beg, M.F., Miller, M.I., Trouvé, A., Younes, L.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int. J. Comput. Vis. 61(2), 139–157 (2005)
Berendsen, F.F., Kotte, A.N.T.J., Viergever, M.A., Pluim, J.P.W.: Registration of organs with sliding interfaces and changing topologies. In: SPIE, vol. 9034, pp. 90340E–90340E-7 (2014)
Delmon, V., Rit, S., Pinho, R., Sarrut, D.: Registration of sliding objects using direction dependent B-splines decomposition. Phys. Med. Biol. 58(5), 1303 (2013)
Kwon, D., Niethammer, M., Akbari, H., Bilello, M., Davatzikos, C., Pohl, K.M.: PORTR: pre-operative and post-recurrence brain tumor registration. IEEE TMI (3), 651–667
Landman, B.A., et al.: MICCAI 2012 Workshop on Multi-Atlas Labeling. CreateSpace, Scotts Valley (2012)
Mueller, S.G., et al.: Ways toward an early diagnosis in Alzheimer’s disease: the Alzheimer’s disease neuroimaging initiative (ADNI). Alzheimer’s Dement. 1(1), 55–66 (2005)
Papież, B.W., Heinrich, M.P., Fehrenbach, J., Risser, L., Schnabel, J.A.: An implicit sliding-motion preserving regularisation via bilateral filtering for deformable image registration. Med. Image Anal. 18(8), 1299–1311 (2014)
Risser, L., Baluwala, H.Y., Schnabel, J.A., Vialard, F.X.: Piecewise-diffeomorphic image registration: application to the motion estimation between 3D CT lung images with sliding conditions. Med. Image Anal. 17(2), 182–193 (2012)
This research was supported by the Lundbeck Foundation and by the Centre for Stochastic Geometry and Advanced Bioimaging, funded by a grant from the Villum Foundation.
Editors and Affiliations
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Nielsen, R.K., Darkner, S., Feragen, A. (2019). TopAwaRe: Topology-Aware Registration. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11765. Springer, Cham. https://doi.org/10.1007/978-3-030-32245-8_41
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32244-1
Online ISBN: 978-3-030-32245-8