X-Ray to CT Rigid Registration Using Scene Coordinate Regression
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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 [46]
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Cites
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 · cited 3× · see citing works
“Landmark estimation methods aim to solve for pose using correspondences between 3D landmark annotations and its estimated 2D projection points [7,3,5], while methods based on pose regression estimate the global camera pose in a single inference [12].”
MethodTowards Fully Automatic X-Ray to CT Registration
Esteban, J., Grimm, M., Unberath, M., Zahnd, G., & Navab, N. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-32226-7_70 · Cited by 38 · see citing works
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Research works printed but not linked: 14
- Aouadi, S., Sarry, L.: Accurate and precise 2D-3D registration based on x-ray intensity. Comput. Vis. Image Underst. 110(1), 134-151 (Apr 2008)
- Belei, P., Skwara, A., De La Fuente, M., Schkommodau, E., Fuchs, S., Wirtz, D.C., Kämper, C., Radermacher, K.: Fluoroscopic navigation system for hip surface replacement. Comput. Aided Surg. 12(3), 160-167 (May 2007)
- Bradley, M.P., Benson, J.R., Muir, J.M.: Accuracy of acetabular component posi- tioning using computer-assisted navigation in direct anterior total hip arthroplasty. Cureus 11(4), e4478 (Apr 2019)
- George, A.K., Sonmez, M., Lederman, R.J., Faranesh, A.Z.: Robust automatic rigid registration of MRI and x-ray using external fiducial markers for XFM-guided interventional procedures. Med. Phys. 38(1), 125-141 (Jan 2011)
- Kendall, A., Grimes, M., Cipolla, R.: PoseNet: A convolutional network for Real- Time 6-DOF camera relocalization. In: 2015 IEEE International Conference on Computer Vision (ICCV). pp. 2938-2946 (Dec 2015)
- Livyatan, H., Yaniv, Z., Joskowicz, L.: Gradient-based 2-D/3-D rigid registration of fluoroscopic x-ray to CT. IEEE Trans. Med. Imaging 22(11), 1395-1406 (Nov 2003)
- Maurer, Jr, C.R., Fitzpatrick, J.M., Wang, M.Y., Galloway, Jr, R.L., Maciunas, R.J., Allen, G.S.: Registration of head volume images using implantable fiducial markers. IEEE Trans. Med. Imaging 16(4), 447-462 (Aug 1997)
- Merloz, P., Troccaz, J., Vouaillat, H., Vasile, C., Tonetti, J., Eid, A., Plaweski, S.: Fluoroscopy-based navigation system in spine surgery. Proc. Inst. Mech. Eng. H 221(7), 813-820 (Oct 2007)
- Sattler, T., Zhou, Q., Pollefeys, M., Leal-Taixé, L.: Understanding the limitations of CNN-Based absolute camera pose regression. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 3297-3307. unknown (Jun 2019)
- Selles, C.A., Beerekamp, M.S.H., Leenhouts, P.A., Segers, M.J.M., Goslings, J.C., Schep, N.W.L., EF3X Study Group: The value of intraoperative 3-dimensional fluoroscopy in the treatment of distal radius fractures: A randomized clinical trial. J. Hand Surg. Am. 45(3), 189-195 (Mar 2020)
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- Woerner, M., Sendtner, E., Springorum, R., Craiovan, B., Worlicek, M., Renkawitz, T., Grifka, J., Weber, M.: Visual intraoperative estimation of cup and stem position is not reliable in minimally invasive hip arthroplasty. Acta Orthop. 87(3), 225-230 (Jun 2016)
- Wylie, J.D., Ross, J.A., Erickson, J.A., Anderson, M.B., Peters, C.L.: Operative fluoroscopic correction is reliable and correlates with postoperative radiographic correction in periacetabular osteotomy. Clin. Orthop. Relat. Res. 475(4), 1100- 1106 (Apr 2017)
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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].”
Background
- Authors
- Shrestha, P., Xie, C., Shishido, H., Yoshii, Y., & Kitahara, I.
- Affiliations
- University of Tsukuba (JP)
- Tokyo Medical University (JP)
- Corresponding author
- Shrestha, Pragyan
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2023
- Pages
- 781-790
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4387225958
- Cited by
- 12
- References
- 2
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 5
- FWCI
- 1.29
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- University of Tsukuba
- Japan Society for the Promotion of Science (23K08618)
- 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
- rigid registration, scene coordinate regression, intraoperative fluoroscopy, perspective-n-point, RANSAC, fully convolutional network, orthopedic surgery, 3D model alignment
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