Abstract
Most existing deep learning-based medical image registration methods estimate a single-directional displacement field between the moving and fixed image pair, resulting in registration errors when there are substantial differences between the to-be-registered image pairs. To solve this issue, we propose a symmetric normalization network to estimate the deformations in a bi-directional way. Specifically, our method learns two bi-directional half-way displacement fields, which warp the moving and fixed images to their mean space. Besides, a symmetric magnitude constraint is designed in the mean space to ensure precise registration. Additionally, a deformation-inverse network is employed to obtain the inverse of the displacement field, which is applied to the inference pipeline to compose the final end-to-end displacement field between the moving and fixed images. During inference, our method first estimates the two half-way displacement fields and then composes one half-way displacement field with the inverse of another half. Moreover, we adopt a multi-level strategy to hierarchically perform registration, for gradually aligning images to their mean space, thereby improving accuracy and smoothness. Experimental results on two datasets demonstrate that the proposed method improves registration performance compared with state-of-the-art algorithms. Our code is available at https://github.com/QingRui-Sha/HSyN.
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
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. Medical Image Analysis 12(1), 26–41 (2008)
Avants, B.B., Tustison, N.J., Song, G., Cook, P.A., Klein, A., Gee, J.C.: A reproducible evaluation of ants similarity metric performance in brain image registration. Neuroimage 54(3), 2033–2044 (2011)
Bajcsy, R., Kovačič, S.: Multiresolution elastic matching. Computer Vision, Graphics, and Image Processing 46(1), 1–21 (1989)
Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: Voxelmorph: a learning framework for deformable medical image registration. IEEE Transactions on Medical Imaging 38(8), 1788–1800 (2019)
Cao, X., Yang, J., Zhang, J., Nie, D., Kim, M., Wang, Q., Shen, D.: Deformable image registration based on similarity-steered cnn regression. In: Medical Image Computing and Computer Assisted Intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part I 20. pp. 300–308. Springer (2017)
Chen, J., Frey, E.C., He, Y., Segars, W.P., Li, Y., Du, Y.: Transmorph: Transformer for unsupervised medical image registration. Medical Image Analysis 82, 102615 (2022)
Crum, W.R., Camara, O., Hawkes, D.J.: Methods for inverting dense displacement fields: Evaluation in brain image registration. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2007: 10th International Conference, Brisbane, Australia, October 29-November 2, 2007, Proceedings, Part I 10. pp. 900–907. Springer (2007)
Dalca, A.V., Balakrishnan, G., Guttag, J., Sabuncu, M.R.: Unsupervised learning for fast probabilistic diffeomorphic registration. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part I. pp. 729–738. Springer (2018)
Fischl, B.: Freesurfer. Neuroimage 62(2), 774–781 (2012)
Greer, H., Tian, L., Vialard, F.X., Kwitt, R., Bouix, S., San Jose Estepar, R., Rushmore, R., Niethammer, M.: Inverse consistency by construction for multistep deep registration. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 688–698. Springer (2023)
Haouchine, N., Dorent, R., Juvekar, P., Torio, E., Wells III, W.M., Kapur, T., Golby, A.J., Frisken, S.: Learning expected appearances for intraoperative registration during neurosurgery. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 227–237. Springer (2023)
Huang, W., Yang, H., Liu, X., Li, C., Zhang, I., Wang, R., Zheng, H., Wang, S.: A coarse-to-fine deformable transformation framework for unsupervised multi-contrast mr image registration with dual consistency constraint. IEEE Transactions on Medical Imaging 40(10), 2589–2599 (2021)
Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. Advances in Neural Information Processing Systems 28 (2015)
Lorenzi, M., Ayache, N., Pennec, X.: Schild’s ladder for the parallel transport of deformations in time series of images. In: Biennial international conference on information processing in medical imaging. pp. 463–474. Springer (2011)
Marcus, D.S., Wang, T.H., Parker, J., Csernansky, J.G., Morris, J.C., Buckner, R.L.: Open access series of imaging studies (oasis): cross-sectional mri data in young, middle aged, nondemented, and demented older adults. Journal of cognitive neuroscience 19(9), 1498–1507 (2007)
Mok, T.C., Chung, A.: Fast symmetric diffeomorphic image registration with convolutional neural networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 4644–4653 (2020)
Mok, T.C., Chung, A.C.: Large deformation diffeomorphic image registration with laplacian pyramid networks. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23. pp. 211–221. Springer (2020)
Sha, Q., Sun, K., Jiang, C., Xu, M., Xue, Z., Cao, X., Shen, D.: Detail-preserving image warping by enforcing smooth image sampling. Neural Networks p. 106426 (2024)
Shrestha, P., Xie, C., Shishido, H., Yoshii, Y., Kitahara, I.: X-ray to ct rigid registration using scene coordinate regression. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 781–790. Springer (2023)
Siebert, H., Hansen, L., Heinrich, M.P.: Fast 3d registration with accurate optimisation and little learning for learn2reg 2021. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 174–179. Springer (2021)
Wang, H., Ni, D., Wang, Y.: Modet: Learning deformable image registration via motion decomposition transformer. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 740–749. Springer (2023)
West, J., Fitzpatrick, J.M., Wang, M.Y., Dawant, B.M., Maurer Jr, C.R., Kessler, R.M., Maciunas, R.J., Barillot, C., Lemoine, D., Collignon, A., et al.: Comparison and evaluation of retrospective intermodality brain image registration techniques. Journal of computer assisted tomography 21(4), 554–568 (1997)
Wodzinski, M., Müller, H.: Invnet: A deep learning approach to invert complex deformation fields. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). pp. 1302–1305. IEEE (2021)
Zhu, Y., Lu, S.: Swin-voxelmorph: A symmetric unsupervised learning model for deformable medical image registration using swin transformer. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 78–87. Springer (2022)
Acknowledgments
This work was supported in part by National Natural Science Foundation of China (grant numbers 62131015, 62250710165, U23A20295), the STI 2030-Major Projects (grant number 2022ZD0209000), Shanghai Municipal Central Guided Local Science and Technology Development Fund (grant number YDZX2023310 0001001), Science and Technology Commission of Shanghai Municipality (STCSM) (grant number 21010502600), and The Key R&D Program of Guangdong Province, China (grant numbers 2023B0303040001, 2021B0101420006).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Ethics declarations
Disclosure of Interests
The authors have no competing interests to declare that are relevant to the content of this article.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Sha, Q. et al. (2024). Hierarchical Symmetric Normalization Registration Using Deformation-Inverse Network. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15002. Springer, Cham. https://doi.org/10.1007/978-3-031-72069-7_62
Download citation
DOI: https://doi.org/10.1007/978-3-031-72069-7_62
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-72068-0
Online ISBN: 978-3-031-72069-7
eBook Packages: Computer ScienceComputer Science (R0)
