Automatic Tracking of the Muscle Tendon Junction in Healthy and Impaired Subjects using Deep Learning

Christoph Leitner*, Robert Jarolim, Andreas Konrad, Annika Kruse, Markus Tilp, Jörg Schröttner, Christian Baumgartner

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Recording muscle tendon junction displacements during movement, allows separate investigation of the muscle and tendon behaviour, respectively. In order to provide a fully-automatic tracking method, we employ a novel deep learning approach to detect the position of the muscle tendon junction in ultrasound images. We utilize the attention mechanism to enable the network to focus on relevant regions and to obtain a better interpretation of the results. Our data set consists of a large cohort of 79 healthy subjects and 28 subjects with movement limitations performing passive full range of motion and maximum contraction movements. Our trained network shows robust detection of the muscle tendon junction on a diverse data set of varying quality with a mean absolute error of 2.55$\pm$1 mm. We show that our approach can be applied for various subjects and can be operated in real-time. The complete software package is available for open-source use via: https://github.com/luuleitner/deepMTJ
Original languageEnglish
Number of pages5
JournalarXiv.org e-Print archive
Publication statusPublished - 5 May 2020

Keywords

  • q-bio.QM
  • cs.LG
  • eess.IV

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