4D-Foot: A Fully Automated Pipeline of Four-Dimensional Analysis of the Foot Bones Using Bi-plane X-Ray Video and CT
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Recent approaches train neural networks to segment intraoperative X-rays and localize predefined landmarks from segmentation masks.
“Recent approaches train neural networks to segment intraoperative X-rays and localize predefined landmarks from the segmentation masks [17, 25, 27, 37, 39].”
From Intraoperative 2D/3D Image Registration via Differentiable X-Ray Rendering (2024) · cites this paper as [37]
Cites
Hiasa, Y., Otake, Y., Takao, M., Ogawa, T., Sugano, N., & Sato, Y. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2940555 · Cited by 157 · see citing works
Self-citationDilated 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
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
Self-citationTowards 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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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
“Recent approaches train neural networks to segment intraoperative X-rays and localize predefined landmarks from the segmentation masks [17, 25, 27, 37, 39].”
Background
- Authors
- Mizoe, S., Otake, Y., Miyamoto, T., Soufi, M., Nakao, S., Tanaka, Y., & Sato, Y.
- Affiliations
- Nara Institute of Science and Technology (JP)
- Nara Medical University (JP)
- Corresponding author
- Otake, Yoshito
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2021
- Pages
- 182-192
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3204164727
- Cited by
- 5
- References
- 4
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 2 · 2023: 1
- FWCI
- 0.25
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Funders
- Japan Society for the Promotion of Science (21K09256)
- 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 › Lower Extremity Biomechanics and Pathologies
- Keywords
- 2D-3D registration, foot bones, tarsal bones, metatarsals, foot biomechanics, reprojection error
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.
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