Multimodal image registration with deep context reinforcement learning
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Ma et al. adopted deep reinforcement learning to extract the best feature representation to minimize discrepancy between CT and MRI images for rigid registration.
“Ma et al. (71) adopted deep reinforcement learning to extract the best feature representation to minimize the discrepancy between CT and MRI images for rigid registration.”
From A review of deep learning-based deformable medical image registration (2022) · cites this paper as [71]
Cites
An artificial agent for robust image registration
Liao, R., Miao, S., De Tournemire, P., Grbic, S., Kamen, A., Mansi, T., & Comaniciu, D. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11230 · Cited by 199 · see citing works
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Transfer Learning in Deep Reinforcement Learning: A Survey
Zhu, Z., Lin, K., Jain, A. K., & Zhou, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3292075 · Cited by 861 · see citing works
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 · see citing works
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 · see citing works
Reinforcement learning for intelligent healthcare applications: A survey
Coronato, A., Naeem, M., De Pietro, G., & Paragliola, G. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101964 · Cited by 320 · see citing works
Survey on deep learning for radiotherapy
Meyer, P., Noblet, V., Mazzara, C., & Lallement, A. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.05.018 · Cited by 302 · see citing works
Medical image registration using deep neural networks: A comprehensive review
Boveiri, H. R., Khayami, R., Javidan, R., & Mehdizadeh, A. (2020) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2020.106767 · Cited by 179 · see citing works
A review of deep learning-based deformable medical image registration
Zou, J., Gao, B., Song, Y., & Qin, J. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.1047215 · Cited by 91 · see citing works
“(71) adopted deep reinforcement learning to extract the best feature representation to minimize the discrepancy between CT and MRI images for rigid registration.”
BackgroundDeep reinforcement learning and its applications in medical imaging and radiation therapy: a survey
Xu, L., Zhu, S., & Wen, N. (2022) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ac9cb3 · Cited by 35 · see citing works
- Authors
- Ma, K., Wang, J., Singh, V., Tamersoy, B., Chang, Y.-J., Wimmer, A., & Chen, T.
- Affiliations
- Siemens (United States) (US)
- Siemens Healthineers (Germany) (DE)
- Corresponding author
- Ma, Kai
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2017
- Pages
- 240-248
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2752460469
- Cited by
- 87
- References
- 1
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 6 · 2023: 14 · 2022: 9 · 2021: 21 · 2020: 20 · 2019: 7 · 2018: 3
- FWCI
- 2.40
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Augmented Reality Applications
- Physical Sciences › Engineering › Biomedical Engineering › Anatomy and Medical Technology
- Keywords
- multimodal image registration, depth images, deep reinforcement learning, contextual information, feature representation learning
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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