Effective Intensity-Based 2D/3D Rigid Registration between Fluoroscopic X-Ray and CT
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The full pairwise analysis yields a ranking of patient-specific methods (rank #1) > foundation model (rank #3) > landmark initialization (rank #4) > landmark regularization (rank #5) > fixed initialization (rank #6), with all differences significant after correction.
“The full pairwise analysis yields a clear ranking: patient-specific methods (rank #1) > foundation model (rank #3) > landmark initialization [6] (rank #4) > landmark regularization [6] (rank #5) > fixed initialization [4,5] (rank #6), with all differences significant after correction.”
From Rapid patient-specific neural networks for intraoperative X-ray to volume registration (2025) · cites this paper as [4]
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Sorted by citations
Russakoff, D. B., Rohlfing, T., Mori, K., Rueckert, D., Ho, A., Adler, J. R., & Maurer, C. R. (2005) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2005.856749 · Cited by 142 · see citing works
Dilated FCN for Multi-Agent 2D/3D Medical Image Registration
Miao, S., Piat, S., Fischer, P., Tuysuzoglu, A., Mewes, P., Mansi, T., & Liao, R. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11576 · Cited by 113 · see citing works
Rapid patient-specific neural networks for intraoperative X-ray to volume registration
Gopalakrishnan, V., Chlorogiannis, D.-D., Abumoussa, A., Larson, A. M., Haouchine, N., Orbach, D. B., Frisken, S., Dey, N., & Golland, P. (2025) · arXiv · doi:10.48550/arxiv.2503.16309 · Cited by 2 · see citing works
- Authors
- Knaan, D., & Joskowicz, L.
- Affiliations
- Hebrew University of Jerusalem (IL)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2003
- Pages
- 351-358
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1520633619
- Cited by
- 43
- References
- 0
- Citations by year
- 2026: 3 · 2024: 3 · 2023: 2 · 2021: 2 · 2020: 1 · 2019: 4 · 2018: 3 · 2017: 2 · 2015: 1 · 2014: 1 · 2013: 1 · 2012: 2
- FWCI
- 2.22
- 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 › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiation Dose and Imaging
- Keywords
- rigid registration, intensity-based registration, fluoroscopy, C-arm, CT, digitally reconstructed radiographs, normalized cross-correlation, genetic search, target registration error, surgical planning, intraoperative imaging
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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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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