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Semi-automated Analysis of Mouse Skeletal Muscle Morphology and Fiber-type Composition
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A Minimal Annotation Pipeline for Deep Learning Segmentation of Skeletal Muscles.

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Summary
This summary is machine-generated.

This study presents an efficient iterative method for automatic skeletal muscle MRI segmentation, significantly reducing manual annotation. The developed model achieves high-quality segmentations comparable to existing methods, enabling clinical translation of quantitative MRI biomarkers.

Keywords:
automatic segmentationneuromuscular disordersnnU‐netquantitative MRIskeletal muscles

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Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Radiology

Background:

  • Translating quantitative skeletal muscle MRI biomarkers into clinical practice necessitates efficient automatic segmentation techniques.
  • Minimizing manual annotation effort is crucial for the widespread adoption of these methods.

Purpose of the Study:

  • To investigate a simple, iterative methodology for building high-quality automatic skeletal muscle MRI segmentation models.
  • To reduce the manual annotation effort required for training segmentation models.

Main Methods:

  • A nnU-Net segmentation model was trained using a retrospective database of 70 quantitative MRI thigh examinations from healthy individuals and patients with neuromuscular diseases.
  • An iterative procedure was employed, progressively adding cases to the training set and using a five-level visual rating scale to assess segmentation quality.
  • Segmentation quality was evaluated on an independent test set (n=20) using Dice coefficient (DICE), 95% Hausdorff distance (HD95), and quantitative biomarkers (CSA, FF, water-T1/T2).

Main Results:

  • High-quality segmentations were achieved (DICE=0.88±0.15/0.86±0.14, HD95=6.35±12.33/6.74±11.57 mm) with a smaller training set (n=30) compared to recent works.
  • Inter-rater agreement on segmentation quality was fair to moderate, with progressive model improvement observed across iterations.
  • Quantitative outcomes showed limited differences from manual delineations (MAD: CSA=65.2 mm², FF=1%, water-T1=8.4 ms, water-T2=0.35 ms), with comparable variability.

Conclusions:

  • The proposed iterative methodology effectively builds high-quality automatic skeletal muscle MRI segmentation models with reduced manual annotation.
  • The model's performance is comparable to state-of-the-art methods, facilitating the clinical translation of quantitative MRI biomarkers for skeletal muscle assessment.
  • The approach demonstrates potential for efficient and reliable segmentation in both healthy and pathological muscle imaging studies.