ArticleComputer methods and programs in biomedicine2021
Automated analysis of medial gastrocnemius muscle-tendon junction displacements in heathy young adults during isolated contractions and walking using deep neural networks.
Article in Computer methods and programs in biomedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 17 citations in OpenAlex.
- Reduced Achilles tendon stiffness in aging associates with higher metabolic cost of walking.Journal of applied physiology (Bethesda, Md. : 1985) · 2024Article
- A comparison of point-tracking algorithms in ultrasound videos from the upper limb.Biomedical engineering online · 2023Article
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- Moving outside the lab: Markerless motion capture accurately quantifies sagittal plane kinematics during the vertical jump.Journal of biomechanics · 2021Article
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3 authors at 1 institution in 1 country.
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Abstract
BACKGROUND AND
objectiveDirect measurement of muscle-tendon junction (MTJ) position is important for understanding dynamic tendon behavior and muscle-tendon interaction in healthy and pathological populations. Traditionally, obtaining MTJ position during functional activities is accomplished by manually tracking the position of the MTJ in cine B-mode ultrasound images - a laborious and time-consuming process. Recent advances in deep learning have facilitated the availability of user-friendly open-source software packages for automated tracking. However, these software packages were originally intended for animal pose estimation and have not been widely tested on ultrasound images. Therefore, the purpose of this paper was to evaluate the efficacy of deep neural networks to accurately track medial gastrocnemius MTJ positions in cine B-mode ultrasound images across tasks spanning controlled loading during isolated contractions to physiological loading during treadmill walking.
methodsCine B-mode ultrasound images of the medial gastrocnemius MTJ were collected from 15 subjects (6M/9F, 23 yr, 71.9 kg, 1.8 m) during treadmill walking at 1.25 m/s and during maximal voluntary isometric plantarflexor contractions (MVICs). Five deep neural networks were trained using 480 manually-labeled images collected during walking, defined as the ground truth, and were then used to predict MTJ position in images from novel subjects: 1) during walking (novel-subject) and 2) during MVICs (novel-condition).
resultsWe found an average mean absolute error of 1.26±1.30 mm and 2.61±3.31 mm between the ground truth and predicted MTJ positions in the novel-subject and novel-condition evaluations, respectively.
conclusionsOur results provide support for the use of open-source software for creating deep neural networks to reliably track MTJ positions in B-mode ultrasound images. We believe this approach to MTJ position tracking is an accessible and time-saving solution, with broad applications for many fields, such as rehabilitation or clinical diagnostics.
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