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Ultrasound Guided Pedicle Screw Entry Point Identification for Spinal Fusion Surgery

5 pagesPublished: October 27, 2019

Abstract

Accurate identification of the location the vertebra and corresponding pedicle is critical during pedicle screw insertion for percutaneous spinal fusion surgery. Currently, two dimensional (2D) fluoroscopy based navigation systems have extensive usage in spinal fusion surgery. Relying on 2D projection images for screw guidance results in high misplacement rates. Furthermore, fluoroscopy-based guidance exposes the surgical staff and patient to harmful ionizing radiation. Real-time non-radiation-based ultrasound (US) is a potential alternative to intra-operative fluoroscopy. However, accurate interpretation of noisy US data and manual operation of the transducer during data collection remains a challenge. In this work we investigate the potential of using multi-modal deep convolutional neural network (CNN) architectures for fully automatic identification of vertebra level and pedicle from US data. Our proposed network achieves 93.54% vertebra identification accuracy on in vivo US data collected from 27 subjects.

Keyphrases: bone shadow, deep learning, local phase, spinal fusion surgery, Ultrasound

In: Patrick Meere and Ferdinando Rodriguez Y Baena (editors). CAOS 2019. The 19th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery, vol 3, pages 306--310

Links:
BibTeX entry
@inproceedings{CAOS2019:Ultrasound_Guided_Pedicle_Screw,
  author    = {Xiao Qi and Michael Vives and Ilker Hacihaliloglu},
  title     = {Ultrasound Guided Pedicle Screw Entry Point Identification for Spinal Fusion Surgery},
  booktitle = {CAOS 2019. The 19th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery},
  editor    = {Patrick Meere and Ferdinando Rodriguez Y Baena},
  series    = {EPiC Series in Health Sciences},
  volume    = {3},
  pages     = {306--310},
  year      = {2019},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-5305},
  url       = {https://easychair.org/publications/paper/98tg},
  doi       = {10.29007/chdq}}
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