Abstract
Physics-inspired regularization is desired for intra-patient image registration since it can effectively capture the biomechanical characteristics of anatomical structures. However, a major challenge lies in the reliance on physical parameters: Parameter estimations vary widely across the literature, and the physical properties themselves are inherently subject-specific. In this work, we introduce a novel data-driven method that leverages hypernetworks to learn the tissue-dependent elasticity parameters of an elastic regularizer. Notably, our approach facilitates the estimation of patient-specific parameters without the need to retrain the network. We evaluate our method on three publicly available 2D and 3D lung CT and cardiac MR datasets. We find that with our proposed subject-specific tissue-dependent regularization, a higher registration quality is achieved across all datasets compared to using a global regularizer. The code is available at https://github.com/compai-lab/2024-miccai-reithmeir.
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
Arratia López, P., Mella, H., Uribe, S., Hurtado, D.E., Sahli Costabal, F.: Warppinn: Cine-mr image registration with physics-informed neural networks. Medical Image Analysis 89, 102925 (2023)
Balakrishnan, G., Zhao, A., Sabuncu, M., Guttag, J., Dalca, A.: Voxelmorph: A learning framework for deformable medical image registration. IEEE Transactions on Medical Imaging 38, 1788–1800 (2019)
Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., Yang, X., Heng, P.A., Cetin, I., Lekadir, K., Camara, O., Ballester, M.A.G., et al.: Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE transactions on medical imaging 37(11), 2514–2525 (2018)
Broit, C.: Optimal Registration of Deformed Images. Graduate School of Arts and Sciences, University of Pennsylvania (1981)
Cocciolone, A.J., Hawes, J.Z., Staiculescu, M.C., Johnson, E.O., Murshed, M., Wagenseil, J.E.: Elastin, arterial mechanics, and cardiovascular disease. American Journal of Physiology-Heart and Circulatory Physiology 315(2), H189–H205 (2018)
Ha, D., Dai, A.M., Le, Q.V.: Hypernetworks. arXiv preprint arXiv:1609.09106 (2017)
Haak, A.J., Tan, Q., Tschumperlin, D.J.: Matrix biomechanics and dynamics in pulmonary fibrosis. Matrix Biology 73, 64–76 (2018)
Hagemann, A., Rohr, K., Stiehl, H., Spetzger, U., Gilsbach, J.: Biomechanical modeling of the human head for physically based, nonrigid image registration. IEEE Transactions on Medical Imaging 18(10), 875–884 (1999)
Hering, A., Hansen, L., Mok, T.C.W., et al.: Learn2reg: Comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning. IEEE Transactions on Medical Imaging 42(3), 697–712 (2023)
Hering, A., Murphy, K., van Ginneken, B.: Learn2reg challenge: Ct lung registration - training data [data set]. zenodo. https://doi.org/10.5281/zenodo.3835682 (2020)
Hoopes, A., Hoffmann, M., Fischl, B., Guttag, J., Dalca, A.V.: Hypermorph: Amortized hyperparameter learning for image registration. Information Processing in Medical Imaging (IPMI), vol. 12729, p. 3-17 (2021)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Min, Z., Baum, Z.M.C., Saeed, S.U., Emberton, M., Barratt, D.C., Taylor, Z.A., Hu, Y.: Non-rigid medical image registration using physics-informed neural networks. In: Information Processing in Medical Imaging (IPMI) (2023)
Mok, T.C.W., Chung, A.C.S.: Large deformation diffeomorphic image registration with laplacian pyramid networks. Medical Image Computing and Computer Assisted Intervention (MICCAI), vol. 12263, pp. 211–221 (2020)
Qin, C., Wang, S., Chen, C., Bai, W., Rueckert, D.: Generative myocardial motion tracking via latent space exploration with biomechanics-informed prior. Medical Image Analysis 83, 102682 (2023)
Ragoza, M., Batmanghelich, K.: Physics-informed neural networks for tissue elasticity reconstruction in magnetic resonance elastography. In: Medical Image Computing and Computer Assisted Intervention (MICCAI). pp. 333–343 (2023)
Reithmeir, A., Schnabel, J.A., Zimmer, V.A.: Learning physics-inspired regularization for medical image registration with hypernetworks. In: Medical Imaging 2024: Image Processing. International Society for Optics and Photonics, SPIE (2024 (accepted))
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI). pp. 234–241 (2015)
Rueckert, D., Schnabel, J.A.: Medical Image Registration, pp. 131–154. Springer, Berlin, Heidelberg (2011)
Talwalkar, J.A.: Elastography for detecting hepatic fibrosis: options and considerations. Gastroenterology 135(1), 299–302 (2008)
Team, N.L.S.T.R.: Data from the national lung screening trial (nlst) [data set]. the cancer imaging archive. (2013)
Wasserthal, J., Breit, H.C., Meyer, M.T., Pradella, M., Hinck, D., Sauter, A.W., Heye, T., Boll, D.T., Cyriac, J., Yang, S., Bach, M., Segeroth, M.: Totalsegmentator: Robust segmentation of 104 anatomic structures in ct images. Radiology: Artificial Intelligence 5(5) (2023)
Zioupos, P.: Ageing human bone: Factors affecting its biomechanical properties and the role of collagen. Journal of Biomaterials Applications 15(3), 187–229 (2001)
Acknowledgments
Anna Reithmeir is funded by the Munich Center for Machine Learning.
Author information
Authors and Affiliations
Corresponding author
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.
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Reithmeir, A., Felsner, L., Braren, R., Schnabel, J.A., Zimmer, V.A. (2024). Data-Driven Tissue- and Subject-Specific Elastic Regularization for Medical Image Registration. 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_54
Download citation
DOI: https://doi.org/10.1007/978-3-031-72069-7_54
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)
