Detecting Anatomical Landmarks for Motion Estimation in Weight-Bearing Imaging of Knees
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The proposed concept has been transferred to projection images of knees to estimate involuntary motion during scans.
“Recently, the proposed concept of view-invariant anatomical landmark detection has been transferred to projection images of knees in an attempt to estimate involuntary motion during scans [3].”
From Learning to detect anatomical landmarks of the pelvis in X-rays from arbitrary views (2019) · cites this paper as [3]
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Cites
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Powers, C. M., Ward, S. R., Fredericson, M., et al. (2003) · Journal of Orthopaedic and Sports Physical Therapy · doi:10.2519/jospt.2003.33.11.677 · Cited by 380 · 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 · cited 1× · see citing works
“We follow the approach discussed in [9, 11] and”
MethodSelf-citationCONRAD—A software framework for cone‐beam imaging in radiology
Maier, A., Hofmann, H. G., Berger, M., Fischer, P., Schwemmer, C., Wu, H., Mueller, K., Joachim Hornegger, A., Choi, J., Rieß, C., et al. (2013) · Medical Physics · doi:10.1118/1.4824926 · Cited by 119 · see citing works
Self-citationX-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgery
Bier, B., Unberath, M., Zaech, J.-N., Fotouhi, J., Armand, M., Osgood, G. M., Navab, N., & Maier, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00937-3_7 · Cited by 98 · see citing works
Self-citationMotion compensation in extremity cone-beam CT using a penalized image sharpness criterion
Sisniega, A., Stayman, J. W., Yorkston, J., Siewerdsen, J. H., & Zbijewski, W. B. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa6869 · Cited by 74 · see citing works
Marker‐free motion correction in weight‐bearing cone‐beam CT of the knee joint
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Self-citationCorrection of patient motion in cone-beam CT using 3D–2D registration
Ouadah, S., Jacobson, M. W., Stayman, J. W., Ehtiati, T., Weiss, C. R., & Siewerdsen, J. H. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa9254 · Cited by 39 · see citing works
Mueller, K., Maier, A., Schwemmer, C., Lauritsch, G., Buck, S. D., Wielandts, J.-Y., Joachim Hornegger, A., & Fahrig, R. (2014) · Physics in Medicine and Biology · doi:10.1088/0031-9155/59/12/3121 · Cited by 19 · see citing works
Self-citationRange Imaging for Motion Compensation in C-Arm Cone-Beam CT of Knees under Weight-Bearing Conditions
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Self-citationA Kernel Ridge Regression Model for Respiratory Motion Estimation in Radiotherapy
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Self-citationMyocardial Twist from X-ray Angiography
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Research works printed but not linked: 1
- Powers, C.M., Ward, S.R., Fredericson, M.: Knee extension in persons with lateral subluxation of the patella : a preliminary study. J. Orthop. Sports Phys. Ther. 33(11), 677-685 (2013)
Cited by
Sorted by citations
Learning to detect anatomical landmarks of the pelvis in X-rays from arbitrary views
Bier, B., Goldmann, F., Zaech, J.-N., Fotouhi, J., Hegeman, R., Grupp, R., Armand, M., Osgood, G., Navab, N., Maier, A., et al. (2019) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-019-01975-5 · Cited by 43 · see citing works
“Recently, the proposed concept of view-invariant anatomical landmark detection has been transferred to projection images of knees in an attempt to estimate involuntary motion during scans [3].”
Self-citationA 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
“Researchers have proposed deep learning models that automatically detect 2D landmarks to solve the PnP problem [12] for pose estimation [13], [14], [15], [16], [17].”
BackgroundSelf-citation
- Authors
- Bier, B., Aschoff, K., Syben, C., Unberath, M., Levenston, M., Gold, G., Fahrig, R., & Maier, A.
- Affiliations
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Johns Hopkins University (US)
- Stanford University (US)
- Corresponding author
- Bier, Bastian
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 83-90
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2889629381
- Cited by
- 16
- References
- 15
- Citations by year
- 2024: 1 · 2023: 2 · 2022: 2 · 2021: 4 · 2020: 3 · 2019: 4
- FWCI
- 6.03
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Health Sciences › Dentistry › Oral Surgery › Dental Radiography and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- anatomical landmark detection, cone-beam computed tomography, motion artifact reduction, fiducial markers, convolutional neural networks, knee joint
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