dc.contributor.author | Ritsche, Paul | |
dc.contributor.author | Seynnes, Olivier Roger | |
dc.contributor.author | Cronin, Neil J. | |
dc.date.accessioned | 2024-02-09T08:50:33Z | |
dc.date.available | 2024-02-09T08:50:33Z | |
dc.date.created | 2023-12-19T08:02:08Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Journal of Open Source Software. 2023, 8(85), Artikkel 5206. | en_US |
dc.identifier.issn | 2475-9066 | |
dc.identifier.uri | https://hdl.handle.net/11250/3116529 | |
dc.description | This work is licensed under a Creative Commons Attribution 4.0 International License. | en_US |
dc.description.abstract | Ultrasonography 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.iso | eng | en_US |
dc.subject | deep learning | en_US |
dc.subject | muscle | en_US |
dc.subject | muscle architecture | en_US |
dc.subject | ultrasonography | en_US |
dc.title | DL_Track_US: A python package to analyse muscle ultrasonography images | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | © 2023 The Authors | en_US |
dc.source.pagenumber | 4 | en_US |
dc.source.volume | 8 | en_US |
dc.source.journal | Journal of Open Source Software | en_US |
dc.source.issue | 85 | en_US |
dc.identifier.doi | 10.21105/joss.05206 | |
dc.identifier.cristin | 2215228 | |
dc.description.localcode | Institutt for fysisk prestasjonsevne / Department of Physical Performance | en_US |
dc.source.articlenumber | 5206 | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |