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Ultrasonography-Based Patellar Tendon Area Measurement: Comparability of Automated vs. Manual Segmentation.

Alberto Guzzi1, Romina Ledergerber2, Oliver Faude2

  • 1Department of Sport, Exercise and Health, University of Basel, Grosse Allee 6, 4052, Basel, Switzerland. alberto.guzzi@unibas.ch.

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Summary

This study introduces an automated tool for segmenting patellar tendon cross-sectional area in ultrasound images, offering a reliable and efficient alternative to manual methods. The developed open-source tool demonstrates good consistency and fast analysis times, aiding clinical and research applications.

Keywords:
Anatomical cross-sectional areaDeep neural networksQuantitative image analysisReliability

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging Analysis
  • Musculoskeletal Ultrasound

Background:

  • Existing tools for muscle anatomical cross-sectional area (ACSA) segmentation are not open-source or peer-reviewed.
  • There is a specific need for an open-source, validated tool for patellar tendon ACSA analysis.
  • Ultrasound imaging is crucial for non-invasive assessment of tendon morphology.

Purpose of the Study:

  • To develop and evaluate an automatic segmentation approach for patellar tendon ACSA in ultrasound images.
  • To assess the reliability and accuracy of the automatic method compared to manual segmentation.
  • To provide a fast, operator-independent tool for patellar tendon analysis in research and clinical settings.

Main Methods:

  • Ultrasound images of the patellar tendon were acquired from 30 participants at 25%, 50%, and 75% of tendon length.
  • Manual segmentation of ACSA was performed to evaluate intra-rater and inter-session reliability.
  • Three neural networks were trained on 497 images to compare manual and automatic segmentation, assessing accuracy metrics like ICC, SEM, and MAE.

Main Results:

  • Good intra-rater reliability (ICC=0.804) and excellent inter-session reliability (ICC=0.980) were observed for manual segmentation.
  • The automatic segmentation showed good comparability with manual analysis (ICC=0.848, MAE=0.05 cm²), with analysis times of 0.302-0.414 seconds per image.
  • A small standardized mean difference (0.53) was found between manual and automatic segmentation after removing erroneous predictions.

Conclusions:

  • The proposed automatic approach provides a fast and less operator-dependent method for patellar tendon ACSA analysis.
  • While minor differences exist compared to manual analysis, the tool offers valuable support for clinical and research applications when used cautiously.
  • This open-source tool addresses the gap for validated patellar tendon segmentation, potentially advancing musculoskeletal research.