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SLIDE: automatic spine level identification system using a deep convolutional neural network

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Percutaneous spinal needle insertion procedures often require proper identification of the vertebral level to effectively and safely deliver analgesic agents. The current clinical method involves “blind” identification of the vertebral level through manual palpation of the spine, which has only 30% reported accuracy. Therefore, there is a need for better anatomical identification prior to needle insertion.

Methods

A real-time system was developed to identify the vertebral level from a sequence of ultrasound images, following a clinical imaging protocol. The system uses a deep convolutional neural network (CNN) to classify transverse images of the lower spine. Several existing CNN architectures were implemented, utilizing transfer learning, and compared for adequacy in a real-time system. In the system, the CNN output is processed, using a novel state machine, to automatically identify vertebral levels as the transducer moves up the spine. Additionally, a graphical display was developed and integrated within 3D Slicer. Finally, an augmented reality display, projecting the level onto the patient’s back, was also designed. A small feasibility study \((n=20)\) evaluated performance.

Results

The proposed CNN successfully discriminates ultrasound images of the sacrum, intervertebral gaps, and vertebral bones, achieving 88% 20-fold cross-validation accuracy. Seventeen of 20 test ultrasound scans had successful identification of all vertebral levels, processed at real-time speed (40 frames/s).

Conclusion

A machine learning system is presented that successfully identifies lumbar vertebral levels. The small study on human subjects demonstrated real-time performance. A projection-based augmented reality display was used to show the vertebral level directly on the subject adjacent to the puncture site.

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Acknowledgements

The authors would like to thank Mehran Pesteie for his help in data collection.

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, The University of British Columbia, 2332 Main Mall, Vancouver, BC, V6T 1Z4, Canada

    Jorden Hetherington, Purang Abolmaesumi & Robert Rohling

  2. Department of Mechanical Engineering, The University of British Columbia, 2332 Main Mall, Vancouver, BC, V6T 1Z4, Canada

    Robert Rohling

  3. Department of Ultrasound, BC Women’s Hospital, 4500 Oak Street, Vancouver, BC, V6H 3N1, Canada

    Victoria Lessoway

  4. Department of Obstetric Anesthesia, BC Women’s Hospital, 4500 Oak Street, Vancouver, BC, V6H 3N1, Canada

    Vit Gunka

Authors
  1. Jorden Hetherington
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  2. Victoria Lessoway
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  3. Vit Gunka
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  4. Purang Abolmaesumi
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  5. Robert Rohling
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Corresponding author

Correspondence to Jorden Hetherington.

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Funding

This study was funded by the Natural Sciences and Engineering Research Council of Canada (Grant Number RGPIN-2-15-03993), and the Canadian Institutes of Health Research (Grant Number MOP-125935).

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Hetherington, J., Lessoway, V., Gunka, V. et al. SLIDE: automatic spine level identification system using a deep convolutional neural network. Int J CARS 12, 1189–1198 (2017). https://doi.org/10.1007/s11548-017-1575-8

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  • DOI: https://doi.org/10.1007/s11548-017-1575-8

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