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Cited by 38Medical Image Segmentation TechniquesHip disorders and treatmentsMedical Imaging and Analysis

Pose Estimation of Periacetabular Osteotomy Fragments With Intraoperative X-Ray Navigation

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Grupp, Robert B., Hegeman, Rachel A., Murphy, Ryan J., Alexander, Clayton P., Otake, Yoshito, McArthur, Benjamin A., Armand, Mehran, Taylor, Russell H.·2020·IEEE Transactions on Biomedical Engineering
DOI 10.1109/tbme.2019.2915165PMC PMC7297497
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Abstract

Objective: State-of-the-art navigation systems for pelvic osteotomies use optical systems with external fiducials. In this paper, we propose the use of X-ray navigation for pose estimation of periacetabular fragments without fiducials. Methods: A two-dimensional/three-dimensional (2-D/3-D) registration pipeline was developed to recover fragment pose. This pipeline was tested through an extensive simulation study and six cadaveric surgeries. Using osteotomy boundaries in the fluoroscopic images, the preoperative plan was refined to more accurately match the intraoperative shape. Results: In simulation, average fragment pose errors were 1.3 °/1.7 mm when the planned fragment matched the intraoperative fragment, 2.2 °/2.1 mm when the plan was not updated to match the true shape, and 1.9 °/2.0 mm when the fragment shape was intraoperatively estimated. In cadaver experiments, the average pose errors were 2.2 °/2.2 mm, 3.8 °/2.5 mm, and 3.5 °/2.2 mm when registering with the actual fragment shape, a preoperative plan, and an intraoperatively refined plan, respectively. Average errors of the lateral center edge angle were less than 20 for all fragment shapes in simulation and cadaver experiments. Conclusion: The proposed pipeline is capable of accurately reporting femoral head coverage within a range clinically identified for long-term joint survivability. Significance: Human interpretation of fragment pose is challenging and usually restricted to rotation about a single anatomical axis. The proposed pipeline provides an intraoperative estimate of rigid pose with respect to all anatomical axes, is compatible with minimally invasive incisions, and has no dependence on external fiducials.

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  • Each contextualization study that considered multiple objects did not explicitly isolate parameters for each object, but used a single NN with output layer indexed by object.

    “However, we found that each of the contextualization studies which considered multiple objects (Grupp et al., 2020c; Doerr et al., 2020; Wang et al., 2020; Esfandiari et al., 2021) did not explicitly isolate the parameters corresponding to each object, but instead used a single NN with data in the output layer indexed according to the appropriate object.”

    From The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective (2021) · cites this paper as Grupp et al., 2020c

    The Materials and Methods section (Intraoperative Registration Strategy; page unavailable) describes successive optimizations of individual objects, followed by simultaneous optimization of their poses. It also specifies different optimization parameters for the pelvis than for the femur and fragment. This contradicts the attribution that the study did not isolate object-specific parameters and instead used a single neural network with object-indexed outputs. (LLM, conf=0.90)

    Checked by a reasoning model — may be wrong.

    Experimental — accuracy not yet measured

  • Iterative optimization with differentiable rendering from a fixed initialization achieved an SMSR of only 7.6%.

    “In contrast, iterative optimization with differentiable rendering from a fixed initialization [20] also does not require patient-specific training, but achieved an SMSR of only 7.6%.”

    From Rapid patient-specific neural networks for intraoperative X-ray to volume registration (2025) · cites this paper as [20]

    The Intraoperative Registration Strategy describes iterative optimization using digitally reconstructed radiographs, supporting that part of the description. It specifies derivative-free optimization and landmark-based initialization, however, not differentiable rendering from a fixed initialization. The Results report other accuracy measures; a search for “7.6” found no SMSR of 7.6%. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • xReg did not achieve sufficient accuracy, producing SMSRs of just 0.3% and 3.3% for the fixed and landmark initializations, respectively.

    “xReg did not achieve sufficient accuracy, producing SMSRs of just 0.3% and 3.3% for the fixed [20] and landmark initializations [5], respectively (Fig. 4a).”

    From Rapid patient-specific neural networks for intraoperative X-ray to volume registration (2025) · cites this paper as [20]

    The Intraoperative Registration Strategy section describes a single-landmark initialization, so this source supports the identification of the landmark method cited as [5]. The Results and Discussion report pose and LCE errors, but do not report a 3.3% SMSR or conclude that xReg using landmark initialization had insufficient accuracy. The sentence's performance figure is therefore not supported by this source. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • Traditional renderers are non-differentiable, so pose estimation is commonly driven by gradient-free optimization.

    “As traditional renderers are non-differentiable, pose estimation is commonly driven by gradient-free optimization [24].”

    From Intraoperative 2D/3D Image Registration via Differentiable X-Ray Rendering (2024) · cites this paper as [24]

    The Intraoperative Registration Strategy section (page unavailable) says the paper uses CMA-ES for pose optimization and explicitly calls it derivative-free; it also uses BOBYQA at a finer resolution. Neither that section nor Related Work (page unavailable) says traditional renderers are non-differentiable or identifies that as the reason for using gradient-free optimization. The paper supports the optimization practice, but not the claimed explanation. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Traditional gradient-free optimization (xReg) successfully recovers the poses of some X-rays but frequently converges to suboptimal minima.

    “Traditional gradient-free optimization (xReg [24]) is able to successfully recover the poses of some X-rays but frequently converges to suboptimal minima.”

    From Intraoperative 2D/3D Image Registration via Differentiable X-Ray Rendering (2024) · cites this paper as [24]

    The Intraoperative Registration Strategy describes derivative-free optimization, and the Results report successful pose estimates alongside some large errors. But the paper does not establish that the method frequently converges to suboptimal minima; it describes CMA-ES as robust to local minima and BOBYQA as less robust, without attributing the observed errors to such convergence. Page numbers are unavailable in our copy. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Grupp, R. B., Hegeman, R. A., Murphy, R. J., Alexander, C. P., Otake, Y., McArthur, B. A., Armand, M., & Taylor, R. H.
Affiliations
  • Johns Hopkins University (US)
  • Nara Institute of Science and Technology (JP)
ORCID
Venue
IEEE Transactions on Biomedical Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2020-02
Volume
67
Issue
2
Pages
441-452
ISSN
  • 0018-9294 (ISSN-L)
  • 1558-2531
DOI
10.1109/tbme.2019.2915165
PMC
PMC7297497
OpenAlex
W2925003514
Cited by
38
References
2
Citations by year
2026: 4 · 2025: 8 · 2024: 4 · 2023: 5 · 2022: 5 · 2021: 3 · 2020: 9
FWCI
1.90
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Biomedical Engineering (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • NIH/NIBIB (R01EB006839, R21EB020113)
  • MEXT/JSPS KAKENHI (26108004)
  • Johns Hopkins University Internal Funds
  • Johns Hopkins University Applied Physics Laboratory
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Health Sciences › Medicine › Surgery › Hip disorders and treatments
  • Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
Keywords
periacetabular osteotomy, fluoroscopy, pelvic osteotomy, femoral head coverage, lateral center-edge angle, preoperative surgical planning, cadaveric surgery
MeSH terms
Acetabulum, Aged, Aged, 80 and over, Female, Fluoroscopy, Hip Joint, Humans, Male, Middle Aged, Osteotomy, Phantoms, Imaging, Imaging, Three-Dimensional, Surgery, Computer-Assisted

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