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Related Experiment Videos

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

Jorden Hetherington1, Victoria Lessoway2, Vit Gunka3

  • 1Department of Electrical and Computer Engineering, The University of British Columbia, 2332 Main Mall, Vancouver, BC, V6T 1Z4, Canada. jordenh@ece.ubc.ca.

International Journal of Computer Assisted Radiology and Surgery
|April 1, 2017
PubMed
Summary

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This summary is machine-generated.

A new machine learning system accurately identifies lumbar vertebral levels using ultrasound images, improving spinal needle insertion safety. This real-time system offers a significant advancement over manual palpation methods.

Area of Science:

  • Medical Imaging
  • Machine Learning
  • Spinal Procedures

Background:

  • Percutaneous spinal procedures require accurate vertebral level identification for safety and efficacy.
  • Current manual palpation methods for identifying vertebral levels have low accuracy (30%).
  • There is a critical need for improved pre-procedural anatomical identification techniques.

Purpose of the Study:

  • To develop and evaluate a real-time system for automatic vertebral level identification using ultrasound images.
  • To enhance the accuracy and safety of percutaneous spinal needle insertion procedures.

Main Methods:

  • A deep convolutional neural network (CNN) was trained to classify transverse ultrasound images of the lower spine.
  • Transfer learning was employed to optimize CNN architectures for real-time performance.
Keywords:
Machine learningNeedle guidanceUltrasoundVertebral level

Related Experiment Videos

  • A novel state machine processed CNN outputs for automatic vertebral level identification.
  • Augmented reality (AR) and graphical displays were developed for visualization.

    • Main Results:

    • The CNN achieved 88% accuracy in discriminating sacrum, intervertebral gaps, and vertebral bones.
    • Real-time processing speeds of 40 frames/s were achieved.
    • 17 out of 20 ultrasound scans demonstrated successful identification of all vertebral levels.

    Conclusions:

    • A machine learning system effectively identifies lumbar vertebral levels from ultrasound data.
    • The system demonstrated real-time performance in a feasibility study with human subjects.
    • An AR display successfully projected the identified vertebral level onto the patient's back.