A Patient-Specific Self-supervised Model for Automatic X-Ray/CT Registration
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Even when supervised training data is augmented with synthetic X-rays, landmark-based localization and pose regression fail to achieve consistent sub-millimeter accuracy.
“Even when supervised training data is augmented with synthetic X-rays, landmark-based localization and pose regression fail to achieve consistent sub-millimeter accuracy [17, 35, 56].”
From Intraoperative 2D/3D Image Registration via Differentiable X-Ray Rendering (2024) · cites this paper as [56]
Typically, a finite number of synthetic X-rays is generated for pretraining, failing to address the central limitation of CNN-based pose regression.
“Typically, a finite number of synthetic X-rays is generated for pretraining [35, 56], failing to address the central limitation of CNN-based pose regression”
From Intraoperative 2D/3D Image Registration via Differentiable X-Ray Rendering (2024) · cites this paper as [56]
Augmenting training data with synthetically generated X-rays helps overcome small clinical sample sizes when training landmark extractors and pose regressors.
“Augmenting training data with synthetically generated X-rays helps overcome the small clinical sample sizes when training landmark extractors [17, 28, 46] and pose regressors [35, 56].”
From Intraoperative 2D/3D Image Registration via Differentiable X-Ray Rendering (2024) · cites this paper as [56]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
DeepDRR – A Catalyst for Machine Learning in Fluoroscopy-Guided Procedures
Unberath, M., Zaech, J.-N., Lee, S. C., Bier, B., Fotouhi, J., Armand, M., & Navab, N. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00937-3_12 · Cited by 145 · see citing works
Self-citationReal-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration
Salehi, S. S. M., Khan, S., Erdogmus, D., & Gholipour, A. (2018) · arXiv · doi:10.48550/arxiv.1803.05982 · Cited by 118 · 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
Unberath, M., Gao, C., Hu, Y., Judish, M., Taylor, R. H., Armand, M., & Grupp, R. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.716007 · Cited by 102 · see citing works
Automatic Annotation of Hip Anatomy in Fluoroscopy for Robust and Efficient 2D/3D Registration
Grupp, R. B., Unberath, M., Gao, C., Hegeman, R. A., Murphy, R. J., Alexander, C. P., Otake, Y., McArthur, B. A., Armand, M., & Taylor, R. H. (2020) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-020-02162-7 · Cited by 81 · see citing works
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Intraoperative 2D/3D Image Registration via Differentiable X-Ray Rendering
Gopalakrishnan, V., Dey, N., & Golland, P. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01108 · Cited by 48 · see citing works
“Augmenting training data with synthetically generated X-rays helps overcome the small clinical sample sizes when training landmark extractors [17, 28, 46] and pose regressors [35, 56].”
BackgroundMethodRapid 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
- Zhang, B., Faghihroohi, S., Azampour, M. F., Liu, S., Ghotbi, R., Schunkert, H., & Navab, N.
- Affiliations
- Deutsches Herzzentrum München (DE)
- Technical University of Munich (DE)
- German Centre for Cardiovascular Research (DE)
- Corresponding author
- Zhang, Baochang
- ORCID
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2023
- Pages
- 515-524
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4387225901
- Cited by
- 27
- References
- 6
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 11
- FWCI
- 2.89
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- self-supervised learning, preoperative CT, multi-head self-attention, incremental learning, pose refinement, minimally invasive surgery
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