“Gold Standard” 2D/3D Registration of X-Ray to CT and MR Images
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Neural networks trained on small datasets for 2D/3D registration fail on new patients or require impractical landmark supervision.
“neural networks trained on small datasets fail on new patients or require impractical landmark supervision”
From Intraoperative 2D/3D Image Registration via Differentiable X-Ray Rendering (2024) · cites this paper as [50]
Only a few small labeled datasets exist for training deep networks for landmark localization, preventing generalization to new patients, procedures, and pathologies.
“While landmark localizers can be learned, only a few small labeled datasets exist for training deep networks [24, 41, 50], preventing the generalization of these methods to new patients, procedures, and pathologies.”
From Intraoperative 2D/3D Image Registration via Differentiable X-Ray Rendering (2024) · cites this paper as [50]
Cites
Predicting error in rigid-body point-based registration
Fitzpatrick, J. M., West, J. B., & Maurer, C. R. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.736021 · Cited by 1,021 · see citing works
A comparison of similarity measures for use in 2-D-3-D medical image registration
Penney, G. P., Weese, J., Little, J. A., Desmedt, P., Hill, D. L. G., & hawkes, D. J. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.730403 · Cited by 782 · see citing works
Retrospective correction of MR intensity inhomogeneity by information minimization
Likar, B., Viergever, M. A., & Pernuš, F. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.974934 · Cited by 270 · see citing works
Self-citationLavallée, S., & Szeliski, R. (1995) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.385980 · Cited by 243 · see citing works
Lemieux, L., Jagoe, R. T., Fish, D. R., Kitchen, N. D., & Thomas, D. G. T. (1994) · Medical Physics · doi:10.1118/1.597276 · Cited by 223 · see citing works
Voxel-based 2-D/3-D registration of fluoroscopy images and CT scans for image-guided surgery
Weese, J., Penney, G., Desmedt, P., Buzug, T. M., Hill, D., & Hawkes, D. J. (1997) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/4233.681173 · Cited by 164 · see citing works
Anatomy-based registration of CT-scan and intraoperative X-ray images for guiding a surgical robot
Gueziec, A. P., Kazanzides, P., Williamson, B., & Taylor, R. H. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.736023 · Cited by 148 · see citing works
The Process and Development of Image-Guided Procedures
Galloway, R. L. (2001) · Annual Review of Biomedical Engineering · doi:10.1146/annurev.bioeng.3.1.83 · Cited by 89 · see citing works
Transgraph: interactive intensity-based 2D/3D registration of x-ray and CT data
LaRose, D., Bayouth, J. E., & Kanade, T. (2000) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.387700 · Cited by 36 · see citing works
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Standardized evaluation methodology for 2-D-3-D registration
van de Kraats, E. B., Penney, G., Tomaževič, D., van Walsum, T., & Niessen, W. J. (2005) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2005.853240 · Cited by 210 · see citing works
Self-citation3-D/2-D registration of CT and MR to X-ray images
Tomaževič, D., Likar, B., Slivnik, T., & Pernuš, F. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.819277 · Cited by 168 · see citing works
Self-citationA protocol for evaluation of similarity measures for rigid registration
Škerl, D., Likar, B., & Pernuš, F. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.874963 · Cited by 93 · see citing works
Self-citationFully automated 2D–3D registration and verification
Varnavas, A., Carrell, T., & Penney, G. (2015) · Medical Image Analysis · doi:10.1016/j.media.2015.08.005 · Cited by 50 · see citing works
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
“Such methods require expert knowledge of landmarks that are visible on X-ray for the specific surgical application and manual annotation of 3D landmarks for every new CT volume [25].”
MethodIntensity-based 2D-3D spine image registration incorporating a single fiducial marker1
Russakoff, D. B., Torsten Rohlfing, B., Adler, J. R., & Maurer, C. R. (2004) · Academic Radiology · doi:10.1016/j.acra.2004.09.013 · Cited by 37 · see citing works
Automatic anatomical labeling method of cerebral arteries in MR-angiography data set.
Takemura, A., Suzuki, M., Harauchi, H., & Okumura, Y. (2006) · PubMed · doi:10.24517/00050490 · Cited by 7 · see citing works
Fast intensity-based fluoroscopy-to-CT registration using pattern search optimization
Roth, M., Dötter, M., Burgkart, R., & Schweikard, A. (2004) · International Congress Series · doi:10.1016/j.ics.2004.03.260 · Cited by 4 · see citing works
Knowledge structure and templates for information elements in validation procedures for CARS
Strauß, M., & Lemke, H. U. (2003) · International Congress Series · doi:10.1016/s0531-5131(03)00523-5 · Cited by 2 · see citing works
- Authors
- Tomaevič, D., Likar, B., & Pernuš, F.
- Affiliations
- University of Ljubljana (SI)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2002
- Pages
- 461-468
- ISSN
- 0302-9743 (ISSN-L)
- OpenAlex
- W1514122536
- Cited by
- 9
- References
- 9
- Citations by year
- 2024: 1 · 2015: 1
- FWCI
- 1.00
- 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 › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Physical Sciences › Engineering › Biomedical Engineering › Anatomy and Medical Technology
- Keywords
- 2D-3D registration, gold standard, image-guided surgery, registration validation, lumbar spine, fiducial markers, target registration error
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