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dc.contributor.authorRitsche, Paul
dc.contributor.authorSeynnes, Olivier Roger
dc.contributor.authorCronin, Neil J.
dc.date.accessioned2024-02-09T08:50:33Z
dc.date.available2024-02-09T08:50:33Z
dc.date.created2023-12-19T08:02:08Z
dc.date.issued2023
dc.identifier.citationJournal of Open Source Software. 2023, 8(85), Artikkel 5206.en_US
dc.identifier.issn2475-9066
dc.identifier.urihttps://hdl.handle.net/11250/3116529
dc.descriptionThis work is licensed under a Creative Commons Attribution 4.0 International License.en_US
dc.description.abstractUltrasonography can be used to assess muscle architectural parameters during static and dynamic conditions. Nevertheless, the analysis of the acquired ultrasonography images presents a major difficulty. Muscle architectural parameters such as muscle thickness, fascicle length and pennation angle are mainly segmented manually. Manual analysis is time expensive, subjective and requires thorough expertise. Within recent years, several algorithms were developed to solve these issues. Yet, these are only partly automated, are not openly available, or lack in user friendliness. The DL_Track_US python package is designed to allow fully automated and rapid analysis of muscle architectural parameters in lower limb ultrasonography images.en_US
dc.language.isoengen_US
dc.subjectdeep learningen_US
dc.subjectmuscleen_US
dc.subjectmuscle architectureen_US
dc.subjectultrasonographyen_US
dc.titleDL_Track_US: A python package to analyse muscle ultrasonography imagesen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© 2023 The Authorsen_US
dc.source.pagenumber4en_US
dc.source.volume8en_US
dc.source.journalJournal of Open Source Softwareen_US
dc.source.issue85en_US
dc.identifier.doi10.21105/joss.05206
dc.identifier.cristin2215228
dc.description.localcodeInstitutt for fysisk prestasjonsevne / Department of Physical Performanceen_US
dc.source.articlenumber5206en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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