A Human-Centered Machine-Learning Approach for Muscle-Tendon Junction Tracking in Ultrasound Images

Christoph Leitner, Robert Jarolim, Bernhard Englmair, Annika Kruse, Karen Andrea Lara Hernandez, Eric Su, Jörg Schröttner, Luke A. Kelly, Glen A. Lichtwark, Markus Tilp, Christian Baumgartner*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Biomechanical and clinical gait research observes muscles and tendons in limbs to study their functions and behaviour. Therefore, movements of distinct anatomical landmarks, such as muscle-tendon junctions, are frequently measured. We propose a reliable and time efficient machine-learning approach to track these junctions in ultrasound videos and support clinical biomechanists in gait analysis. In order to facilitate this process, a method based on deep-learning was introduced. We gathered an extensive data set, covering 3 functional movements, 2 muscles, collected on 123 healthy and 38 impaired subjects with 3 different ultrasound systems, and providing a total of 66864 annotated ultrasound images in our network training. Furthermore, we used data collected across independent laboratories and curated by researchers with varying levels of experience. For the evaluation of our method a diverse test-set was selected that is independently verified by four specialists. We show that our model achieves similar performance scores to the four human specialists in identifying the muscle-tendon junction position. Our method provides time-efficient tracking of muscle-tendon junctions, with prediction times of up to 0.078 seconds per frame (approx. 100 times faster than manual labeling). All our codes, trained models and test-set were made publicly available and our model is provided as a free-to-use online service on https://deepmtj.org/.
Original languageEnglish
JournalIEEE Transactions on Biomedical Engineering
Volume2021
Early online date24 Nov 2021
DOIs
Publication statusE-pub ahead of print - 24 Nov 2021

Keywords

  • Anatomical Landmark Detection
  • Attention Mechanism
  • Convolutional Neural Network
  • Domain Generalization
  • Feature Extraction
  • Instruments
  • Junctions
  • Label Noise
  • Locomotion
  • Muscles
  • Myotendinous Junction
  • Probability Map
  • Segmentation
  • Sequential Learning
  • Soft Labeling
  • Tendons
  • Training
  • U-Net
  • Ultrasonic imaging
  • Videos

ASJC Scopus subject areas

  • Biomedical Engineering

Fields of Expertise

  • Human- & Biotechnology

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