Abstract
Multimodal groupwise registration aligns internal structures in a group of medical images. Current approaches to this problem involve develo** similarity measures over the joint intensity profile of all images, which may be computationally prohibitive for large image groups and unstable under various conditions. To tackle these issues, we propose BInGo, a general unsupervised hierarchical Bayesian framework based on deep learning, to learn intrinsic structural representations to measure the similarity of multimodal images. Particularly, a variational auto-encoder with a novel posterior is proposed, which facilitates the disentanglement learning of structural representations and spatial transformations, and characterizes the imaging process from the common structure with shape transition and appearance variation. Notably, BInGo is scalable to learn from small groups, whereas being tested for large-scale groupwise registration, thus significantly reducing computational costs. We compared BInGo with five iterative or deep-learning methods on three public intrasubject and intersubject datasets, i.e. BraTS, MS-CMR of the heart, and Learn2Reg abdomen MR-CT, and demonstrated its superior accuracy and computational efficiency, even for very large group sizes (e.g., over 1300 2D images from MS-CMR in each group).
X. Wang and X. Luo—Equal Contribution.
This work was funded by the National Natural Science Foundation of China (grant No. 61971142 and 62111530195) and Fujian Provincial Natural Science Foundation project (2021J02019).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ashburner, J.: A fast diffeomorphic image registration algorithm. Neuroimage 38(1), 95–113 (2007)
Avants, B., Gee, J.C.: Geodesic estimation for large deformation anatomical shape averaging and interpolation. Neuroimage 23, S139–S150 (2004)
Baid, U., et al.: The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification. ar**v preprint ar**v:2107.02314 (2021)
Bhatia, K.K., Hajnal, J.V., Puri, B.K., Edwards, A.D., Rueckert, D.: Consistent groupwise non-rigid registration for atlas construction. In: 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro, pp. 908–911. IEEE (2004)
Cang, W.G., You, T., Seo, S., Kwak, S., Han, B.: Domain-specific batch normalization for unsupervised domain adaptation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7354–7362 (2019)
Che, T., et al.: DGR-Net: deep groupwise registration of multispectral images. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 706–717. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20351-1_55
Dalca, A.V., Balakrishnan, G., Guttag, J., Sabuncu, M.R.: Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces. Med. Image Anal. 57, 226–236 (2019)
Geng, X., Christensen, G.E., Gu, H., Ross, T.J., Yang, Y.: Implicit reference-based group-wise image registration and its application to structural and functional MRI. Neuroimage 47(4), 1341–1351 (2009)
He, Z., Chung, A.C.: Unsupervised end-to-end groupwise registration framework without generating templates. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 375–379. IEEE (2020)
Hering, A., et al.: Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning. IEEE Trans. Med. Imaging 42(3), 697–712 (2022)
Hu, Y., et al.: Weakly-supervised convolutional neural networks for multimodal image registration. Med. Image Anal. 49, 1–13 (2018)
Joshi, S.C., Davis, B.C., Jomier, M., Gerig, G.: Unbiased diffeomorphic atlas construction for computational anatomy. Neuroimage 23, S151–S160 (2004)
Kavur, A.E., Selver, M.A., Dicle, O., Barı, M., Gezer, N.S.: CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data, April 2019. https://doi.org/10.5281/zenodo.3362844
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations (2015)
Learned-Miller, E.G.: Data driven image models through continuous joint alignment. IEEE Trans. Pattern Anal. Mach. Intell. 28(2), 236–250 (2005)
Liao, S., Jia, H., Wu, G., Shen, D.: A novel framework for longitudinal atlas construction with groupwise registration of subject image sequences. Neuroimage 59(2), 1275–1289 (2012)
Lorenzen, P., Prastawa, M., Davis, B., Gerig, G., Bullitt, E., Joshi, S.: Multi-modal image set registration and atlas formation. Med. Image Anal. 10(3), 440–451 (2006)
Luo, X., Zhuang, X.: X-metric: an n-dimensional information-theoretic framework for groupwise registration and deep combined computing. IEEE Trans. Pattern Anal. Mach. Intell. (2022)
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2014)
Metz, C.T., Klein, S., Schaap, M., van Walsum, T., Niessen, W.J.: Nonrigid registration of dynamic medical imaging data using nD+ t B-splines and a groupwise optimization approach. Med. Image Anal. 15(2), 238–249 (2011)
Oktay, O., et al.: Attention u-net: learning where to look for the pancreas. In: Medical Imaging with Deep Learning (2018)
Orchard, J., Mann, R.: Registering a multisensor ensemble of images. IEEE Trans. Image Process. 19(5), 1236–1247 (2009)
Paszke, A., et al.: Automatic differentiation in pytorch (2017)
Polfliet, M., Klein, S., Huizinga, W., Paulides, M.M., Niessen, W.J., Vandemeulebroucke, J.: Intrasubject multimodal groupwise registration with the conditional template entropy. Med. Image Anal. 46, 15–25 (2018)
Shi, Y., Paige, B., Torr, P., et al.: Variational mixture-of-experts autoencoders for multi-modal deep generative models. Adv. Neural Inf. Process. Syst. 32, 15718–15729 (2019)
Vahdat, A., Kautz, J.: Nvae: a deep hierarchical variational autoencoder. ar**v preprint ar**v:2007.03898 (2020)
Wachinger, C., Navab, N.: Simultaneous registration of multiple images: similarity metrics and efficient optimization. IEEE Trans. Pattern Anal. Mach. Intell. 35(5), 1221–1233 (2012)
Zhuang, X., et al.: Cardiac segmentation on late gadolinium enhancement MRI: a benchmark study from multi-sequence cardiac MR segmentation challenge. Med. Image Anal. 81, 102528 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, X., Luo, X., Zhuang, X. (2023). BInGo: Bayesian Intrinsic Groupwise Registration via Explicit Hierarchical Disentanglement. In: Frangi, A., de Bruijne, M., Wassermann, D., Navab, N. (eds) Information Processing in Medical Imaging. IPMI 2023. Lecture Notes in Computer Science, vol 13939. Springer, Cham. https://doi.org/10.1007/978-3-031-34048-2_25
Download citation
DOI: https://doi.org/10.1007/978-3-031-34048-2_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-34047-5
Online ISBN: 978-3-031-34048-2
eBook Packages: Computer ScienceComputer Science (R0)