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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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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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