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Automatic Measurement of Pennation Angle from Ultrasound Images using Resnets.

Weimin Zheng1, Shangkun Liu1, Qing-Wei Chai1

  • 1College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China.

Ultrasonic Imaging
|February 10, 2021
PubMed
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This study introduces an automated deep learning method for measuring muscle pennation angles from ultrasound images. The approach accurately detects muscle fiber orientation, achieving results comparable to manual measurements.

Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Deep Learning Applications

Background:

  • Accurate measurement of pennation angle is crucial for understanding muscle mechanics.
  • Manual measurement of pennation angle from ultrasound images can be time-consuming and subjective.
  • Existing automated methods may lack robustness and accuracy.

Purpose of the Study:

  • To develop and validate an automatic deep learning-based approach for measuring pennation angle from ultrasound images.
  • To improve the accuracy and efficiency of pennation angle measurements.
  • To provide a robust tool for quantitative muscle analysis.

Main Methods:

  • Utilized Local Radon Transform (LRT) for detecting superficial and deep aponeuroses.
  • Employed Deep Residual Networks (Resnets) to determine muscle fiber orientation relative to a reference line.
Keywords:
Radon transformdeep residual networksmuscle fiber orientationpennation angleultrasound image

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  • Iteratively revised the reference line to align with muscle fiber orientation for angle calculation.

    • Main Results:

    • The proposed method achieved an average angular difference of approximately 1° compared to manual labeling.
    • Average inference time was 1.6 seconds per single image and 0.47 seconds for sequential image sequences on a CPU.
    • Demonstrated accurate and robust measurements of pennation angle.

    Conclusions:

    • The developed deep learning approach offers an accurate and efficient method for automated pennation angle measurement.
    • The technique shows potential for quantitative analysis in various applications, including sports science and clinical diagnostics.
    • This automated method can overcome limitations of manual measurements, enhancing reliability and speed.