The DOI matches its Crossref record; 9 of the 10 references it cites were found in a registry or an index.
Deep Learning Based Inter-modality Image Registration Supervised by Intra-modality Similarity
Open accessAbstract
Non-rigid inter-modality registration can facilitate accurate information fusion from different modalities, but it is challenging due to the very different image appearances across modalities. In this paper, we propose to train a non-rigid inter-modality image registration network, which can directly predict the transformation field from the input multimodal images, such as CT and MR images. In particular, the training of our inter-modality registration network is supervised by intra-modality similarity metric based on the available paired data, which is derived from a pre-aligned CT and MR dataset. Specifically, in the training stage, to register the input CT and MR images, their similarity is evaluated on the warped MR image and the MR image that is paired with the input CT. So that, the intra-modality similarity metric can be directly applied to measure whether the input CT and MR images are well registered. Moreover, we use the idea of dual-modality fashion, in which we measure the similarity on both CT modality and MR modality. In this way, the complementary anatomies in both modalities can be jointly considered to more accurately train the inter-modality registration network. In the testing stage, the trained inter-modality registration network can be directly applied to register the new multimodal images without any paired data. Experimental results have shown that, the proposed method can achieve promising accuracy and efficiency for the challenging non-rigid inter-modality registration task and also outperforms the state-of-the-art approaches.
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“In medical imaging applications with human tissues, registration of slices must be performed in an elastic form [195].”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [195]
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A global optimisation method for robust affine registration of brain images
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End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network
de Vos, B. D., Berendsen, F. F., Viergever, M. A., Staring, M., & Išgum, I. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67558-9_24 · Cited by 456
Non-parametric Diffeomorphic Image Registration with the Demons Algorithm
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Deformable Image Registration Using a Cue-Aware Deep Regression Network
Cao, X., Yang, J., Zhang, J., Wang, Q., Yap, P., & Shen, D. G. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2822826 · Cited by 123
Self-citationRegion-Adaptive Deformable Registration of CT/MRI Pelvic Images via Learning-Based Image Synthesis
Cao, X., Yang, J., Gao, Y., Wang, Q., & Shen, D. G. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2820424 · Cited by 51
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TransMorph: Transformer for unsupervised medical image registration
Chen, J., Frey, E. C., He, Y., Segars, W. P., Li, Y., & Du, Y. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102615 · Cited by 883
AI in Medical Imaging Informatics: Current Challenges and Future Directions
Panayides, A. S., Amini, A., Filipovic, N. D., Sharma, A., Tsaftaris, S. A., Young, A., Foran, D., Do, N., Golemati, S., Kurc, T., et al. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2991043 · Cited by 713
Deep learning in medical image registration: a survey
Haskins, G., Kruger, U., & Yan, P. (2020) · Machine Vision and Applications · doi:10.1007/s00138-020-01060-x · Cited by 704
Deep learning in medical image registration: a review
Fu, Y., Lei, Y., Wang, T., Curran, W. J., Liu, T., & Yang, X. (2020) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ab843e · Cited by 661
A review of multimodal image matching: Methods and applications
Jiang, X., Ma, J., Xiao, G., Shao, Z., & Guo, X. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.02.012 · Cited by 561
Metadata
- Authors
- Cao, X., Yang, J., Wang, L., Xue, Z., Wang, Q., & Shen, D.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Northwestern Polytechnical University (CN)
- United Imaging Intelligence (China) (CN)
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Shen, Dinggang
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Volume
- 11046
- Pages
- 55-63
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- PMC
- PMC6516490
- OpenAlex
- W2798741430
- Cited by
- 123
- References
- 10
- Citations by year
- 2026: 7 · 2025: 16 · 2024: 19 · 2023: 21 · 2022: 18 · 2021: 14 · 2020: 16 · 2019: 11 · 2018: 1
- FWCI
- 3.16
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Lecture notes in computer science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Institute on Aging (RF1AG053867)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- non-rigid registration, CT-MRI registration, paired training data, multimodal image fusion, deep learning registration
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