Patch-Based Image Similarity for Intraoperative 2D/3D Pelvis Registration During Periacetabular Osteotomy
Open accessThe claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Conventional intensity-based 2D/3D registration approaches suffer from a limited capture range due to local minima in hand-crafted image similarity functions.
“Conventional intensity-based 2D/3D registration approaches suffer from a limited capture range due to the presence of local minima in hand-crafted image similarity functions.”
From A Fully Differentiable Framework for 2D/3D Registration and the Projective Spatial Transformers (2024) · cites this paper as [8]
The abstract (p. 1) says existing similarity metrics struggle with the mismatch between an intact preoperative pelvis and a fractured intraoperative pelvis. Section 2.1 (pp. 3–4) describes intensity-based registration and a limitation of normalized cross-correlation, but neither passage addresses limited capture range caused by local minima. Searches for “capture range” and “local minima” found no passage supporting that explanation. (LLM, conf=0.72)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
No references could be resolved yet.
Research works printed but not linked: 5
- [8]8. Markelj, P., Tomaˇ zeviˇ c, D., Likar, B., Pernuˇ s, F.: A review of 3d/2d registration methods for image-guided interventions. Med Image Anal 16 (3) (2012) 642-661
- [9]9. Gong, R.H., Stewart, J., Abolmaesumi, P.: Multiple-object 2-d-3-d registration for noninvasive pose identification of fracture fragments. IEEE Trans. Biomed. Eng. 58 (6) (2011) 1592-1601
- [11]11. Knaan, D., Joskowicz, L.: Effective intensity-based 2d/3d rigid registration between fluoroscopic x-ray and ct. In: Proc. Med. Image Comput. Comput.-Assist. Interv, Springer (2003) 351-358
- [12]12. Otake, Y., Armand, M., Armiger, R., Kutzer, M., Basafa, E., Kazanzides, P., Taylor, R.: Intraoperative image-based multi-view 2d/3d registration for imageguided orthopaedic surgery: Incorporation of fiducial-based c-arm tracking and gpu-acceleration. IEEE Trans. Med. Imag. 31 (4) (2012) 948-962
- [13]13. Krˇ cah, M., Sz´ ekely, G., Blanc, R.: Fully automatic and fast segmentation of the femur bone from 3d-ct images with no shape prior. In: Proc. IEEE Intl. Symp. Biomed. Imag. (2011) 2087-2090
Cited by
Sorted by citations
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
“In this paper, S is defined as the weighted sum of normalized cross-correlations of 2D image gradients computed over image patches [22].”
MethodSelf-citationIntraoperative 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
“Initialized at a PA pose, xReg uses a multi-stage gradient-free optimization routine and a patch-based image-gradient NCC similarity metric [23].”
MethodA 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
“…the state-of-the-art image intensity-based 2D/3D registration method as benchmark, 2 which uses Patch-based Gradient Normalized Cross Correlation (P-Grad-NCC) [8] score as the similarity metric and “Covariance Matrix Adaptation: Evolutionary Search” (CMAES) [40] as the optimization strategy.”
MethodSelf-citationKu, P.-C., Martin-Gomez, A., Gao, C., Grupp, R., Mears, S. C., & Armand, M. (2023) · Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization · doi:10.1080/21681163.2022.2152375 · Cited by 8 · see citing works
“The normalized gradient cross-correlation (Grad-NCC) method is used to calculate the similarity scores between the image patches (Grupp et al. 2018).”
MethodSelf-citation
- Authors
- Grupp, R. B., Armand, M., & Taylor, R. H.
- Affiliations
- Johns Hopkins University (US)
- Corresponding author
- Grupp, Robert B.
- ORCID
- Grupp, Robert B.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 153-163
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2894976108
- Cited by
- 23
- References
- 0
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 4 · 2023: 2 · 2022: 3 · 2021: 1 · 2020: 2 · 2019: 1
- FWCI
- 0.30
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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
- periacetabular osteotomy, fluoroscopic navigation, intensity-based registration, computer-assisted surgery
About these checks
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
