Abstract
Medical image registration is a fundamental task for a wide range of clinical procedures. Automatic systems have been developed for image registration, where the majority of solutions are supervised techniques. However, those techniques rely on a large and well-representative corpus of ground truth, which is a strong assumption in the medical domain. To address this challenge, we propose a novel unified unsupervised framework for image registration and segmentation. The highlight of our framework is that patch-based representation is key for performance gain. We first propose a patch-based contrastive strategy that enforces locality conditions and richer feature representation. Secondly, we propose a patch stitching strategy to eliminate artifacts. We demonstrate, through our experiments, that our technique outperforms current state-of-the-art unsupervised techniques.
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
Balakrishnan, G., et al.: Voxelmorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imaging 38(8), 1788–1800 (2019)
Kim, B., et al.: Cyclemorph: cycle consistent unsupervised deformable image registration. Med. Image Anal. 71, 102036 (2021)
Liu, L., Aviles-Rivero, A.I., Schönlieb, C.B.: Contrastive registration for unsupervised medical image segmentation. arXiv preprint arXiv:2011.08894 (2020)
Liu, L., Hu, X., Zhu, L., Heng, P.-A.: Probabilistic multilayer regularization network for unsupervised 3D brain image registration. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 346–354. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_39
Liu, L., Huang, Z., Liò, P., Schönlieb, C.B., Aviles-Rivero, A.I.: Pc-swinmorph: patch representation for unsupervised medical image registration and segmentation. arXiv preprint arXiv:2203.05684 (2022)
Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. arXiv preprint arXiv:2103.14030 (2021)
Mok, T.C., Chung, A.: Fast symmetric diffeomorphic image registration with convolutional neural networks. In: CVPR, pp. 4644–4653 (2020)
Ye, M., et al.: Deeptag: an unsupervised deep learning method for motion tracking on cardiac tagging magnetic resonance images. In: CVPR, pp. 7261–7271 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, L., Huang, Z., Liò, P., Schönlieb, CB., Aviles-Rivero, A.I. (2022). You only Look at Patches: A Patch-wise Framework for 3D Unsupervised Medical Image Registration. In: Hering, A., Schnabel, J., Zhang, M., Ferrante, E., Heinrich, M., Rueckert, D. (eds) Biomedical Image Registration. WBIR 2022. Lecture Notes in Computer Science, vol 13386. Springer, Cham. https://doi.org/10.1007/978-3-031-11203-4_21
Download citation
DOI: https://doi.org/10.1007/978-3-031-11203-4_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-11202-7
Online ISBN: 978-3-031-11203-4
eBook Packages: Computer ScienceComputer Science (R0)