Hostname: page-component-848d4c4894-2pzkn Total loading time: 0 Render date: 2024-06-02T00:36:32.268Z Has data issue: false hasContentIssue false
  • English
  • Français

Investigating the vision-based intervertebral motion estimation of the Cadaver’s craniovertebral junction

Published online by Cambridge University Press:  29 May 2023

Mohammad Zubair ,
Sachin Kansal Open the ORCID record for Sachin Kansal [Opens in a new window]  and
Sudipto Mukherjee
Mohammad Zubair
Affiliation:
Mechanical Engineering Department, Indian Institute of Technology Delhi, New Delhi, India
Sachin Kansal*
Affiliation:
Computer Science Engineering Department, Thapar Institute of Engineering Technology, Patiala, Punjab, India
Sudipto Mukherjee
Affiliation:
Mechanical Engineering Department, Indian Institute of Technology Delhi, New Delhi, India
*
Corresponding author: Sachin Kansal; Email: sachin.kansal@thapar.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Craniovertebral junction (CVJ) is one of the more complex parts of the spinal column. It provides mobility to the cranium and houses the spinal cord. In a healthy subject, the CVJ contributes 25% of the flexion–extension motion and 50% of the axial rotation of the neck. This work reports instrumentation development and results for evaluating implant performance in the stabilized CVJ after surgical procedures. Typically, some bony parts of the vertebrae causing compression to the spinal cord are removed and subsequently stabilized by the instrumenting implant in the CVJ. Pose estimation of the Cadaveric CVJ region is estimated using a monocular vision-based setup. The cervical spine’s first three vertebrae surround the CVJ area, where most cervical spine mobility originates. We aim to evaluate the performance of vision-based intervertebral motion estimation of the Cadaver’s CVJ in the Indian population, particularly in older people. A series of tests were performed on the Cadaver’s CVJ to evaluate the vision system-based motion estimation performance.

Keywords

Cadaverparallel manipulatormonocular cameraArUco libraryintervertebral motionpose estimationkinematics
Type
Research Article
Information
Robotica , Volume 41 , Issue 10 , October 2023 , pp. 2907 - 2914
Copyright
© The Author(s), 2023. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Schulze, M., Trautwein, F., Vordemvenne, T., Raschke, M. and Heuer, F., “A method to perform spinal motion analysis from functional X-ray images,” J. Biomech. 44(9), 1740–1746 (2011).CrossRefGoogle ScholarPubMed
Puttlitz, C. M., Melcher, R. P., Kleinstueck, F. S., Harms, J., Bradford, D. S. and Lotz, J. C., “’Stability analysis of craniovertebral junction fixation techniques,” J. Bone Joint Surg. 86(3), 561–568 (2004).CrossRefGoogle ScholarPubMed
Helgeson, M. D., Lehman, R. A., Sasso, R. C., Dmitriev, A. E., Mack, A. W. and Riew, K. D., “Biomechanical analysis of occipitocervical stability afforded by three fixation techniques,” Spine J. 11(3), 245–250 (2011).CrossRefGoogle ScholarPubMed
Dvorak, J., Antinnes, J. A., Punjabi, M., Oustalot, D. and Bonomo, M., “Age and gender-related normal cervical spine motion,” J. Neurosurg. Spine 17(Supplement), S393–S398 (1992).Google Scholar
Tsai, L.-W.. Robot Analysis: The Mechanics of Serial and Parallel Manipulators (John Wiley & Sons, New York, NY, 1999).Google Scholar
Garrido-Jurado, S., Muñoz-Salinas, R., Madrid-Cuevas, F. J. and Marín-Jiménez, M. J., “Automatic generation and detection of highly reliable fiducial markers under occlusion,” Pattern Recognit. 47(6), 2280–2292 (2014).CrossRefGoogle Scholar
Garrido-Jurado, S., Muñoz-Sainas, R., Madrid-Cuevas, F. J. and Medina-Carnicer, R., “Generation of fiducial marker dictionaries using mixed integer linear programming,” Pattern Recognit. 51, 481–491 (2016).CrossRefGoogle Scholar
Kansal, S., Kumar, R. and Mukherjee, S., “Color invariant state estimator to predict the object trajectory and catch using dexterous multi-fingered delta robot architecture,” J. Multimed. Tools Appl. 80(8), 11865–11886 (2021). https://doi.org/10.1007/s11042-020-09937-9.CrossRefGoogle Scholar
Kansal, S. and Mukherjee, S., “Vision-based manipulation of a regular shaped object,” J. Procedia Comput. Sci. 84, 142–146 (2015). https://doi.org/10.1016/j.procs.2016.04.079.CrossRefGoogle Scholar
Zubair, M., Kansal, S. and Mukherjee, S., “Vision-based pose e stimation of cranio cervical region: Experimental setup and saw bone based study,” J. Robot. 40, 2031–2046 (2022).Google Scholar
Kansal, S. and Mukherjee, S., “Vision-based kinematic analysis of the Delta robot for object catching,” J. Robot. 40(6), 2010–2030 (2022).CrossRefGoogle Scholar
Kansal, S., “Vision-based forc e distribution analysis for object catching,” J. Electron. Imaging 30(5), 053019 (2021).Google Scholar
Reddy, O. J., Kavitha, P., Gafoor, A., Suresh, B. and Harinath, P., “Radiological evaluation of craniovertebral junction anomalies,” J. Evid.-Based Med. Healthcare 2(35), 5472–5498 (2015).Google Scholar
Hong, J. T., Kim, I. S., Lee, H. J., Park, J. H., Hur, J. W., Lee, J. B., Lee, J. J. and Lee, S. H., “Evaluation and surgical planning for craniovertebral junction deformity,” J. Neurospine 17(3), 554–567 (2020). https://doi.org/10.14245/ns.2040510.255.CrossRefGoogle ScholarPubMed
Garrido-Jurado, S., Munoz-Salinas, R., Madrid-Cuevas, F. J. and Marin-Jimenez, M. J., “Automatic generation and detection of highly reliable fiducial markers under occlusion,” Pattern Recognit. 47(6), 2280–2292 (2014).CrossRefGoogle Scholar
Kansal, S. and Mukherjee, S., “Automatic single-view monocular camera calibration-based object manipulation using novel dexterous multi-fingered delta robot,” J. Neural Comput. Appl. 31(7), 2661–2678 (2018).CrossRefGoogle Scholar
Basler, A. G.. Global Manufacturer of Digital Cameras (Cambridge University Press, Cambridge, 2018).Google Scholar
Babinec, A., Jurišica, L., Hubinský, P. and Duchoň, F., “Visual localisation of mobile robot using artificial markers,” Procedia Eng. 96, 1–9 (2014).CrossRefGoogle Scholar