Abstract
Brain MR image registration is challenging due to the large inter-subject anatomical variation. Especially, the highly convoluted brain cortex makes it difficult to accurately align the corresponding structures of the underlying images. In this paper, we propose a novel deep learning strategy to simplify the image registration task. Specifically, we train a morphological simplification network (MS-Net), which can generate a simplified image with fewer anatomical details given a complex input image. With this trained MS-Net, we can reduce the complexity of both the fixed and the moving images and iteratively derive their respective trajectories of gradually simplified images. The generated images at the ends of the two trajectories are so simple that they are very similar in appearance and morphology and thus easy to register. In this way, these two trajectories can act as a bridge to link the fixed and the moving images and guide their registration. Our experiments show that the proposed method can achieve more accurate registration results than state-of-the-art methods. Moreover, the proposed method can be generalized to the unseen dataset without the need for re-training or domain adaptation.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic demons: efficient non-parametric image registration. NeuroImage 45(1), S61–S72 (2009)
Beg, M.F., et al.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int. J. Comput. Vis. 61(2), 139–157 (2005)
Avants, B.B., et al.: A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54(3), 2033–2044 (2011)
Hamm, J., Ye, D.H., Verma, R., Davatzikos, C.: GRAM: a framework for geodesic registration on anatomical manifolds. Med. Image Anal. 14(5), 633–642 (2010)
Wang, Q., et al.: Predict brain MR image registration via sparse learning of appearance and transformation. Med. Image Anal. 20(1), 61–75 (2015)
Shen, D., Davatzikos, C.: HAMMER: hierarchical attribute matching mechanism for elastic registration. IEEE Trans. Med. Imaging 21(11), 1421–1439 (2002)
Joshi, A.A., et al.: Surface-constrained volumetric brain registration using harmonic mappings. IEEE Trans. Med. Imaging 26, 1657–1668 (2007)
Zhang, J., Wang, Q., Wu, G., Shen, D.: Cross-manifold guidance in deformable registration of brain MR images. In: Zheng, G., Liao, H., Jannin, P., Cattin, P., Lee, S.-L. (eds.) MIAR 2016. LNCS, vol. 9805, pp. 415–424. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-43775-0_38
Haskins, G., Uwe, K., Yan, P.: Deep learning in medical image registration: a survey. arXiv:1903.02026 (2019)
Balakrishnan, G., et al.: An unsupervised learning model for deformable medical image registration. In: CVPR, pp. 9252–9260 (2018)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Taubin, G.: Curve and surface smoothing without shrinkage. In: ICCV, pp. 852–857 (1995)
Cao, X., et al.: Deformable image registration using a cue-aware deep regression network. IEEE Trans. Biomed. Eng. 65(9), 1900–1911 (2018)
Christensen, G.E., et al.: Introduction to the non-rigid image registration evaluation project (NIREP). In: Pluim, J.P.W., Likar, B., Gerritsen, F.A. (eds.) WBIR 2006. LNCS, vol. 4057, pp. 128–135. Springer, Heidelberg (2006). https://doi.org/10.1007/11784012_16
Shattuck, D.W., et al.: Construction of a 3D probabilistic atlas of human cortical structures. Neuroimage 39(3), 1064–1080 (2008)
Acknowledgement
This work was partially supported by the National Key Research and Development Program of China (2018YFC0116400) and STCSM (19QC1400600).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Wei, D. et al. (2019). Morphological Simplification of Brain MR Images by Deep Learning for Facilitating Deformable Registration. In: Suk, HI., Liu, M., Yan, P., Lian, C. (eds) Machine Learning in Medical Imaging. MLMI 2019. Lecture Notes in Computer Science(), vol 11861. Springer, Cham. https://doi.org/10.1007/978-3-030-32692-0_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-32692-0_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32691-3
Online ISBN: 978-3-030-32692-0
eBook Packages: Computer ScienceComputer Science (R0)