Automatic Inference and Measurement of 3D Carpal Bone Kinematics From Single View Fluoroscopic Sequences
Open accessAbstract
We present a novel framework for estimating the 3D poses and shapes of the carpal bones from single view fluoroscopic sequences. A hybrid statistical model representing both the pose and shape variation of the carpal bones is built, based on a number of 3D CT data sets obtained from different subjects at different poses. Given a fluoroscopic sequence, the wrist pose, carpal bone pose and bone shapes are estimated iteratively by matching the statistical model with the 2D images. A specially designed cost function enables smoothed parameter estimation across frames and constrains local bone pose with a penalty term. We have evaluated the proposed method on both simulated data and real fluoroscopic sequences and demonstrated that the relative poses of carpal bones can be accurately estimated. One condition that may be assessed using this measurement is dissociation, where the distance between the bones is larger than normal. Scaphoid-Lunate dissociation is one of the most common of these. The error of the measured 3D Scaphoid-Lunate distances were 0.75±0.50 mm for simulated data (25 subjects) and 0.93±0.47 mm for real data (15 subjects). We also propose a method for constructing a "standard" pathology measurement tool for automatically detecting Scaphoid-Lunate dissociation conditions, based on single-view fluoroscopic sequences. For the simulated data, it produced 100% sensitivity and specificity. For the real data, it achieved 83% sensitivity and 78% specificity.
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A 2D intraoperative X-ray can be registered to a 3D preoperative CT volume by maximizing its similarity to a rendered X-ray with respect to the camera pose.
“A 2D intraoperative X-ray can be registered to a 3D preoperative CT volume by maximizing its similarity to a rendered X-ray with respect to the camera pose [8, 14, 34].”
From Intraoperative 2D/3D Image Registration via Differentiable X-Ray Rendering (2024) · cites this paper as [14]
Traditional intensity-based registration methods are routinely employed to solve compound, multiple object registration problems across broad applications.
“Despite the small number of learning-based multiple object registration strategies, traditional intensity-based registration methods are now routinely employed to solve compound, multiple object, registration problems across broad applications, such as: kinematic measurements of bone (Chen et al., 2012; Otake et al., 2016; Abe et al., 2019), rib motion and respiratory analysis (Hiasa et al., 2019)”
From The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective (2021) · cites this paper as Chen et al., 2012
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- Authors
- Chen, X., Graham, J., Hutchinson, C., & Muir, L.
- Affiliations
- Manchester Academic Health Science Centre (GB)
- University of Warwick (GB)
- Salford Royal NHS Foundation Trust (GB)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2013-02
- Volume
- 32
- Issue
- 2
- Pages
- 317-328
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2036207440
- Cited by
- 26
- References
- 24
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 2 · 2023: 1 · 2022: 2 · 2021: 3 · 2020: 1 · 2019: 3 · 2018: 2 · 2016: 3 · 2015: 1 · 2014: 2 · 2013: 2
- FWCI
- 1.65
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Repository@Nottingham (University of Nottingham) (acceptedVersion, other-oa)
- Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
- Nottingham ePrints (University of Nottingham) (submittedVersion)
- Research Explorer (The University of Manchester) (submittedVersion, other-oa)
- Funders
- Medical Research Council
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Health Sciences › Medicine › Surgery › Orthopedic Surgery and Rehabilitation
- Keywords
- carpal bones, carpal kinematics, fluoroscopy, 3D pose estimation, shape estimation, statistical shape models, wrist
- MeSH terms
- Algorithms, Artificial Intelligence, Carpal Bones, Fluoroscopy, Movement, Pattern Recognition, Automated, Posture, Radiographic Image Enhancement, Radiographic Image Interpretation, Computer-Assisted, Sensitivity and Specificity, Reproducibility of Results, Imaging, Three-Dimensional
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