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Cited by 10Medical Imaging and AnalysisMedical Image Segmentation TechniquesSpinal Fractures and Fixation Techniques

Data-driven detection and registration of spine surgery instrumentation in intraoperative images

Open access
Doerr, Sophia A., Uneri, Ali, Huang, Yixuan, Jones, Craig K., Zhang, Xiaoxuan, Ketcha, Michael D., Helm, Patrick, Siewerdsen, Jeffrey H.·2020·Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling
DOI 10.1117/12.2550052PMC PMC9450103
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Abstract

Purpose. Conventional model-based 3D-2D registration algorithms can be challenged by limited capture range, model validity, and stringent intraoperative runtime requirements. In this work, a deep convolutional neural network was used to provide robust initialization of a registration algorithm (known-component registration, KC-Reg) for 3D localization of spine surgery implants, combining the speed and global support of data-driven approaches with the previously demonstrated accuracy of model-based registration. Methods. The approach uses a Faster R-CNN architecture to detect and localize a broad variety and orientation of spinal pedicle screws in clinical images. Training data were generated using projections from 17 clinical cone-beam CT scans and a library of screw models to simulate implants. Network output was processed to provide screw count and 2D poses. The network was tested on two test datasets of 2,000 images, each depicting real anatomy and realistic spine surgery instrumentation – one dataset involving the same patient data as in the training set (but with different screws, poses, image noise, and affine transformations) and one dataset with five patients unseen in the test data. Assessment of device detection was quantified in terms of accuracy and specificity, and localization accuracy was evaluated in terms of intersection-overunion (IOU) and distance between true and predicted bounding box coordinates. Results. The overall accuracy of pedicle screw detection was ~86.6% (85.3% for the same-patient dataset and 87.8% for the many-patient dataset), suggesting that the screw detection network performed reasonably well irrespective of disparate, complex anatomical backgrounds. The precision of screw detection was ~92.6% (95.0% and 90.2% for the respective same-patient and many-patient datasets). The accuracy of screw localization was within 1.5 mm (median difference of bounding box coordinates), and median IOU exceeded 0.85. For purposes of initializing a 3D-2D registration algorithm, the accuracy was observed to be well within the typical capture range of KC-Reg.1 Conclusions. Initial evaluation of network performance indicates sufficient accuracy to integrate with algorithms for implant registration, guidance, and verification in spine surgery. Such capability is of potential use in surgical navigation, robotic assistance, and data-intensive analysis of implant placement in large retrospective datasets. Future work includes correspondence of multiple views, 3D localization, screw classification, and expansion of the training dataset to a broader variety of anatomical sites, number of screws, and types of implants.

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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 Doerr et al., 2020

    The Methods section (page unavailable) describes one Faster R-CNN that handles multiple regions of interest, and the Discussion says the training images contained two screws. That supports the shared-network, multiple-object part of the claim. The Methods describe outputs per region of interest and bounding-box coordinates by class, however, not an output layer explicitly indexed by individual object. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 3

  1. [5]Suzani A, Seitel A, Liu Y, Fels S, Roholing RN. Fast automatic vertebrae detection and localization in pathological CT scans – a deep learning approach. In: MICCAI.; 2015.
  2. [7]Kügler D, Stefanov A, Mukhopadhyay A. i3PosNet: Instrument Pose Estimation from X-Ray. IEEE Trans Med Imaging. 2018 http://arxiv.org/abs/1802.09575. PMC7316684 32440956 · arXiv
  3. [11]Ren S, He K, Girshick R, Sun J. Faster r-cnn:Towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems.; 2015: 91– 99.

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Related

Authors
Doerr, S. A., Uneri, A., Huang, Y., Jones, C. K., Zhang, X., Ketcha, M. D., Helm, P., & Siewerdsen, J. H.
Affiliations
  • Johns Hopkins University (US)
  • Medtronic (United States) (US)
Corresponding author
Siewerdsen, Jeffrey H.
ORCID
Venue
Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling
Publisher
SPIE
Published
2020-03-16
Volume
11315
Pages
91
DOI
10.1117/12.2550052
PMC
PMC9450103
OpenAlex
W3010790897
Cited by
10
References
8
Citations by year
2026: 3 · 2024: 1 · 2023: 1 · 2022: 1 · 2021: 4
FWCI
2.72
Type
conference paper
Language
en
Open access
Locations
  • SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
National Institute of Biomedical Imaging and Bioengineering (R01EB017226)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Health Sciences › Medicine › Surgery › Spinal Fractures and Fixation Techniques
Keywords
spinal surgery, 2D-3D registration, implant localization, intraoperative imaging, Faster R-CNN, cone-beam computed tomography, surgical navigation, robotic assistance

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