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Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography
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Summary

This study developed an automated deep learning method for segmenting gluteus medius (GMd) muscles in CT scans, proving it's as accurate as manual segmentation while saving time and effort for muscle volume assessment.

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Area of Science:

  • Medical imaging analysis
  • Deep learning applications in healthcare
  • Musculoskeletal research

Background:

  • Accurate gluteus medius (GMd) volume assessment is crucial for analyzing muscle atrophy and patient recovery.
  • Manual segmentation of GMd regions in CT images is time-consuming and labor-intensive.
  • Automating this process can significantly improve efficiency in clinical practice.

Purpose of the Study:

  • To develop and evaluate a deep learning-based method for automated gluteus medius (GMd) segmentation in CT images.
  • To compare the accuracy of automated segmentation with manual segmentation using Dice Similarity Coefficient (DSC), Volume Similarity (VS), and Shape Similarity (MS).
  • To confirm the non-inferiority of the automated method for cubic muscle volume assessment.

Main Methods:

  • Utilized a conditional generative adversarial network (GAN) for image segmentation.
  • Trained the model on 5250 augmented CT image pairs from patients with hip osteoarthritis.
  • Compared automated segmentation results against manual segmentation on test datasets.

Main Results:

  • The automated method achieved an average DSC of 0.748, compared to 0.812 for manual segmentation (p < 0.0001).
  • Volume Similarity (VS) and Shape Similarity (MS) showed no significant differences between automated and manual methods (p = 0.069 and p = 0.308, respectively).
  • The non-inferiority of the automated segmentation's DSC was statistically verified against manual segmentation.

Conclusions:

  • The proposed GAN-based automated GMd segmentation technique is non-inferior to manual segmentation.
  • This automated method significantly reduces the time and effort required for muscle volume assessment.
  • The findings support the use of this technique for accurate and efficient evaluation of gluteus medius muscle volume.