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Related Experiment Video

Updated: Dec 7, 2025

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
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Supervised segmentation framework for evaluation of diffusion tensor imaging indices in skeletal muscle.

Laura Secondulfo1, Augustin C Ogier2,3, Jithsa R Monte4

  • 1Department of Biomedical Engineering and Physics, Amsterdam University Medical Centers, University of Amsterdam, The Netherlands.

NMR in Biomedicine
|October 1, 2020
PubMed
Summary

A new semi-automatic segmentation pipeline significantly reduces time for analyzing diffusion tensor imaging (DTI) muscle data. This method accurately quantifies DTI indices in upper leg muscles, aiding in disease diagnosis and recovery monitoring.

Keywords:
applicationsdiffusion tensor imaging (DTI)methods and engineeringmusclemusculoskeletalpost-acquisition processingquantitation

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Area of Science:

  • Medical Imaging
  • Musculoskeletal System Imaging
  • Diffusion Tensor Imaging (DTI)

Background:

  • DTI is crucial for diagnosing muscle diseases and tracking recovery.
  • Manual segmentation of leg muscles for DTI quantification is laborious and time-consuming.
  • Developing efficient automated methods is essential for clinical application.

Purpose of the Study:

  • To assess the effectiveness of a supervised semi-automatic segmentation pipeline for DTI quantification in upper leg muscles.
  • To compare the accuracy and efficiency of semi-automatic versus manual segmentation methods.
  • To evaluate the impact of segmentation on key DTI indices (MD, FA, λ3).

Main Methods:

  • Longitudinal MRI datasets (DTI and Dixon) from 11 subjects (baseline, post-marathon, follow-up) were analyzed.
  • Semi-automatic segmentation utilized transversal and longitudinal propagation on Dixon images.
  • Manual segmentation served as the gold standard for comparison.
  • Dice Similarity Coefficients (DSC), Bland-Altman, and regression analyses were performed.

Main Results:

  • The semi-automatic method achieved an average DSC of 0.92 ± 0.01, indicating high agreement with manual segmentation.
  • Bland-Altman analysis showed minimal bias (0.5%–3.0%) in DTI indices (MD, FA, λ3).
  • Regression analysis revealed strong correlations (r=0.99) for all DTI indices, confirming accuracy.
  • Segmentation time was reduced threefold with the semi-automatic approach.

Conclusions:

  • The supervised semi-automatic segmentation pipeline accurately quantifies DTI indices in upper leg muscles.
  • This method significantly reduces segmentation time compared to manual approaches.
  • The pipeline offers a promising tool for efficient DTI analysis in clinical settings and research.