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Related Experiment Video

Updated: Jan 26, 2026

Optical Cross-Sectional Muscle Area Determination of Drosophila Melanogaster Adult Indirect Flight Muscles
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Automatic Tracking of Muscle Cross-Sectional Area Using Convolutional Neural Networks with Ultrasound.

Xin Chen1, Chenxi Xie1, Zhewei Chen1

  • 1School of Biomedical Engineering, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, China.

Journal of Ultrasound in Medicine : Official Journal of the American Institute of Ultrasound in Medicine
|April 3, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method using convolutional neural networks (CNNs) for tracking muscle cross-sectional area (CSA) in ultrasound images. The CNN approach demonstrated high accuracy and efficiency for muscle CSA estimation during contractions.

Keywords:
convolutional neural networkdeep learningmuscle cross-sectional areaultrasound imaging

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Area of Science:

  • Medical imaging
  • Biomedical engineering
  • Machine learning in healthcare

Background:

  • Accurate measurement of muscle cross-sectional area (CSA) is crucial for assessing muscle function and health.
  • Manual tracking of muscle CSA in ultrasound (US) images is time-consuming and prone to inter-observer variability.
  • Developing automated methods can improve the efficiency and reliability of muscle CSA analysis.

Purpose of the Study:

  • To develop an automated muscle cross-sectional area (CSA) tracking method using convolutional neural networks (CNNs) on ultrasound (US) images.
  • To evaluate the performance of the proposed CNN-based method for muscle segmentation and CSA calculation.
  • To compare the developed method against state-of-the-art muscle segmentation techniques.

Main Methods:

  • A convolutional neural network (CNN) was designed with two stages: feature extraction and score map reconstruction.
  • The CNN was trained in three steps using US image sequences of the rectus femoris muscle during voluntary contraction.
  • Segmentation performance was assessed using 5-fold cross-validation, calculating mean precision, recall, and Dice's coefficient (DSC).

Main Results:

  • The proposed CNN method achieved high segmentation performance with a mean precision of 0.936 ± 0.029, recall of 0.882 ± 0.045, and DSC of 0.907 ± 0.023.
  • The CNN-based approach demonstrated superior performance compared to a state-of-the-art constrained mutual-information-based free-form deformation method.
  • The automated tracking method proved effective in segmenting the rectus femoris muscle in real-time US sequences.

Conclusions:

  • The developed automated method offers an accurate and efficient solution for estimating muscle CSA during muscle contractions.
  • CNNs provide a powerful tool for automating muscle segmentation and analysis in ultrasound imaging.
  • This technique has the potential to enhance clinical assessments of muscle status and performance.