Abstract
The variational registration model takes advantage of explaining uncertainties of registration results. However, most existing variational registration models are based on convolutional neural networks (CNNs), which cannot capture distant information in images. Besides, the evidence lower bound (ELBO) and the commonly used standard prior cannot close the gap between the real posterior and the variational posterior in the vanilla variational registration model. This paper proposes a network in a variational image registration model for cardiac motion estimation to effectively capture the spatial correspondence of long-distance images and solve the shortcomings of CNNs. Our proposed network comprises a Transformer with a T2T module and the cross attention between the moving and the fixed images. To close the gap between the real posterior and the variational posterior, the importance-weighted evidence lower bound (iwELBO) is introduced into the variational registration model with an implicit prior. The coefficients of a parametric transformation using multi-supports CSRBFs are latent variables in our variational registration model, which improve registration accuracy significantly. Experimental results show that the proposed method outperforms state-of-arts research on public cardiac datasets.
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
References
Abdar, M., et al.: A review of uncertainty quantification in deep learning: techniques, applications and challenges. Inf. Fusion 76, 243–297 (2021)
Akkari, N., Casenave, F., Daniel, T., Ryckelynck, D.: Data-targeted prior distribution for variational autoencoder. Fluids (2021)
Andreopoulos, A., Tsotsos, J.K.: Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI. Med. Image Anal. 12(3), 335–357 (2008)
Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: An unsupervised learning model for deformable medical image registration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9252–9260 (2018)
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)
Bernard, O., et al.: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans. Med. Imaging 37(11), 2514–2525 (2018)
Burda, Y., Grosse, R., Salakhutdinov, R.: Importance weighted autoencoders. arXiv preprint arXiv:1509.00519 (2015)
Campello, V.M., et al.: Multi-centre, multi-vendor and multi-disease cardiac segmentation: the M &Ms challenge. IEEE Trans. Med. Imaging 9458279 (2021)
Chen, J., Frey, E.C., He, Y., Segars, W.P., Li, Y., Du, Y.: TransMorph: transformer for unsupervised medical image registration. arXiv preprint arXiv:2111.10480 (2021)
Connor, M., Canal, G.H., Rozell, C.J.: Variational autoencoder with learned latent structure. ArXiv abs/2006.10597 (2021)
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
Fan, J., Cao, X., Xue, Z., Yap, P.-T., Shen, D.: Adversarial similarity network for evaluating image alignment in deep learning based registration. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 739–746. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_83
Gan, Z., Sun, W., Liao, K., Yang, X.: Probabilistic modeling for image registration using radial basis functions: Application to cardiac motion estimation. IEEE Trans. Neural Netw. Learn. Syst. (2022)
Gong, X., Khaidem, L., Zhu, W., Zhang, B., Doermann, D.: Uncertainty learning towards unsupervised deformable medical image registration. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 2484–2493 (2022)
Huang, C.W., Sankaran, K., Dhekane, E., Lacoste, A., Courville, A.: Hierarchical importance weighted autoencoders. In: International Conference on Machine Learning, pp. 2869–2878. PMLR (2019)
Kim, B., Kim, D.H., Park, S.H., Kim, J., Lee, J.G., Ye, J.C.: CycleMorph: cycle consistent unsupervised deformable image registration. Med. Image Anal. 71, 102036 (2021)
Krebs, J., Mansi, T., Mailhé, B., Ayache, N., Delingette, H.: Unsupervised probabilistic deformation modeling for robust diffeomorphic registration. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 101–109. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_12
Liu, R., Li, Z., Zhang, Y., Fan, X., Luo, Z.: Bi-level probabilistic feature learning for deformable image registration. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 723–730 (2021)
Radau, P., Lu, Y., Connelly, K., Paul, G., Dick, A., Wright, G.: Evaluation framework for algorithms segmenting short axis cardiac MRI. The MIDAS J.-Cardiac MR Left Ventricle Segment. Challenge 49 (2009)
Sandkühler, R., Andermatt, S., Bauman, G., Nyilas, S., Jud, C., Cattin, P.C.: Recurrent registration neural networks for deformable image registration. Adv. Neural Inf. Process. Syst. 32 (2019)
Sedghi, A., Kapur, T., Luo, J., Mousavi, P., Wells, W.M.: Probabilistic image registration via deep multi-class classification: characterizing uncertainty. In: Greenspan, H., et al. (eds.) CLIP/UNSURE -2019. LNCS, vol. 11840, pp. 12–22. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32689-0_2
Sheikhjafari, A., Noga, M., Punithakumar, K., Ray, N.: Unsupervised deformable image registration with fully connected generative neural network (2018)
Takahashi, H., Iwata, T., Yamanaka, Y., Yamada, M., Yagi, S.: Variational autoencoder with implicit optimal priors. In: AAAI (2019)
Tomczak, J.M., Welling, M.: VAE with a VampPrior. In: AISTATS (2018)
Xu, H., Chen, W., Lai, J., Li, Z., Zhao, Y., Pei, D.: On the necessity and effectiveness of learning the prior of variational auto-encoder. ArXiv abs/1905.13452 (2019)
Yuan, L., et al.: Tokens-to-token ViT: training vision transformers from scratch on ImageNet. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 558–567 (2021)
Zhao, S., Dong, Y., Chang, E.I., Xu, Y., et al.: Recursive cascaded networks for unsupervised medical image registration. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10600–10610 (2019)
Acknowledgements
This paper is supported by the Shenzhen Fundamental Research Program (JCYJ20220531102407018).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Xu, K., Huang, Q., Yang, X. (2023). Importance Weighted Variational Cardiac MRI Registration Using Transformer and Implicit Prior. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14229. Springer, Cham. https://doi.org/10.1007/978-3-031-43999-5_55
Download citation
DOI: https://doi.org/10.1007/978-3-031-43999-5_55
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43998-8
Online ISBN: 978-3-031-43999-5
eBook Packages: Computer ScienceComputer Science (R0)
