Robust non-rigid registration through agent-based action learning
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Krebs et al. applied RL to deformable prostate MRI registration by training an agent to investigate the parametric space of a statistical deformation model constructed with training data.
“In 2017, Krebs et al. (72) applied RL to deformable prostate MRI registration by training an agent to investigate the parametric space of a statistical deformation model constructed with training data.”
From A review of deep learning-based deformable medical image registration (2022) · cites this paper as [72]
Cites
Flownet: Learning optical flow with convolutional networks
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Smagt, P. V. D., Cremers, D., & Brox, T. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.316 · Cited by 4,313 · see citing works
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
Self-citationFast Predictive Image Registration
Yang, X., Kwitt, R., & Niethammer, M. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46976-8_6 · Cited by 127 · see citing works
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A deep learning framework for unsupervised affine and deformable image registration
de Vos, B. D., Berendsen, F. F., Viergever, M. A., Sokooti, H., Staring, M., & Išgum, I. (2019) · Medical Image Analysis · doi:10.1016/j.media.2018.11.010 · Cited by 818 · 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
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
A Fully Differentiable Framework for 2D/3D Registration and the Projective Spatial Transformers
Gao, C., Feng, A., Liu, X., Taylor, R. H., Armand, M., & Unberath, M. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3299588 · Cited by 36 · see citing works
“Several researchers formulated such learning-based 2D/3D registration with reinforcement learning paradigms [22], [23], [24], but they still perform pose regression at each iteration.”
Background(2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-66182-7 · Cited by 23 · see citing works
- Authors
- Krebs, J., Mansi, T., Delingette, H., Zhang, L., Ghesu, F. C., Miao, S., Maier, A. K., Ayache, N., Liao, R., & Kamen, A.
- Affiliations
- Institut national de recherche en sciences et technologies du numérique (FR)
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Corresponding author
- Krebs, Julian
- ORCID
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2017
- Pages
- 344-352
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2753461941
- Cited by
- 225
- References
- 3
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 19 · 2023: 34 · 2022: 39 · 2021: 44 · 2020: 35 · 2019: 29 · 2018: 14 · 2017: 2
- FWCI
- 6.40
- Type
- conference paper
- Language
- en
- Open access
- green
- inria.hal.science
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- HAL (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- deformable image registration, agent-based learning, deep learning, statistical deformation model, synthetic training data, prostate MRI, inter-subject registration, atlas-based segmentation, motion compensation
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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.
Statistics and arithmetic are recomputed, not judged.
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