Literature DB >> 34215012

Semi-automated Tracing of Hamstring Muscle Architecture for B-mode Ultrasound Images.

Kevin Cronin1, Eamonn Delahunt2, Shane Foley1, Giuseppe De Vito2,3, Conor McCarthy4, Sean Cournane5.   

Abstract

Hamstring strains are the most prevalent injury sustained by field-sport athletes. Insufficiencies in the architectural characteristics of the hamstring muscles can heighten an athlete's risk of incurring a hamstring strain. To evaluate the influence of hamstring muscle architectural characteristics (i. e., fascicle length, pennation angle, muscle thickness) on injury risk, it is necessary to precisely evaluate these characteristics. Considering this, our aim was to develop and evaluate the precision of a novel semi-automated tracing software to measure the architectural characteristics of the biceps femoris long head (the most commonly injured hamstring muscle) in B-mode ultrasound images. We acquired static sonograms of the biceps femoris long head from ten healthy male field-sport athletes. The architectural characteristics (fascicle length, pennation angle, and muscle thickness) of participants' biceps femoris long head were evaluated 10 times using the tracing software, with the specific purpose of determining its measurement precision. The tracing software precisely measured the architectural characteristics of the participants' biceps femoris long head: fascicle length (% CV: 0.64-1.12), pennation angle (% CV: 2.58-10.70), muscle thickness (% CV: 0.48-2.04) Our semi-automated skeletal muscle tracing algorithm precisely measures fascicle length, pennation angles, and muscle thickness of the biceps femoris long head in static B-mode ultrasound images. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany.

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Year:  2021        PMID: 34215012     DOI: 10.1055/a-1493-3082

Source DB:  PubMed          Journal:  Int J Sports Med        ISSN: 0172-4622            Impact factor:   3.118


  1 in total

1.  Reliability and accuracy of ultrasound image analyses completed manually versus an automated tool.

Authors:  Kealey J Wohlgemuth; Malia N M Blue; Jacob A Mota
Journal:  PeerJ       Date:  2022-06-16       Impact factor: 3.061

  1 in total

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