  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 dataset, 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/.

Computer Vision and Pattern Recognition;Quantitative Methods