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[{"key": "dc.contributor.advisor", "value": "Terziyan, Vagan", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Cronin, Neil", "language": "", "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2020-04-15T05:08:55Z", "language": null, "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2020-04-15T05:08:55Z", "language": null, "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2020", "language": "", "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/68521", "language": null, "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Ultra\u00e4\u00e4nt\u00e4 k\u00e4ytet\u00e4\u00e4n paljon tuki- ja liikuntaelimist\u00f6n kudosten kuvaamiseen, etuina ollen matalat kustannukset, siirrett\u00e4vyys sek\u00e4 ei-invasiivisuus. Ultra\u00e4\u00e4nikuvien analysointi vaatii edelleen kehityst\u00e4. Viime aikoihin saakka kaikki analyysit on tehty manuaalisesti, mik\u00e4 on hyvin subjektiivista ja aikaa viev\u00e4\u00e4. T\u00e4ll\u00e4 hetkell\u00e4 on olemassa muutamia t\u00e4h\u00e4n tarkoitukseen suunniteltu-ja avoimen l\u00e4hdekoodin menetelmi\u00e4. T\u00e4m\u00e4nhetkiset l\u00e4hestymistavat perustuvat yleens\u00e4 s\u00e4\u00e4nt\u00f6pohjaisiin analyyseihin. Useat niist\u00e4 tekev\u00e4t virheit\u00e4, jos analysoitavissa kuvissa on suuria poikkeamia menetelm\u00e4n kehityksess\u00e4 k\u00e4ytettyihin kuviin verrattuna. Viime vuosina syv\u00e4oppimisen k\u00e4ytt\u00e4minen l\u00e4\u00e4ketieteellisiss\u00e4 kuva-analyyseiss\u00e4 on tuottanut erinomaisia segmentointituloksia eri kuvantamistavoilla, mutta useimpia niist\u00e4 ei ole viel\u00e4 sovellettu laajemmin tuki- ja liikuntaelimist\u00f6n ultra\u00e4\u00e4nikuviin. T\u00e4ss\u00e4 tutkielmassa esit-telen syv\u00e4oppimiseen perustuvan automatisoidun menetelm\u00e4n, joka laskee lihasten rakenteeseen liittyvi\u00e4 parametreja ultra\u00e4\u00e4nikuvista ja videoista. T\u00e4m\u00e4 menetelm\u00e4 perustuu U-net arkkitehtuuriin, ja mallit on opetettu k\u00e4sin merkityill\u00e4 lihassolukimppujen ja aponeuroosien kuvilla. N\u00e4iden opetettujen mallien avulla tehtiin pikselitasoinen semanttinen segmentointi uusille kuville luokittelemalla jokainen pikseli yhteen kolmesta mahdollisesta luokasta (lihassolukimppu, aponeuroosi, muu). Tuloksia verrattiin kahden eri tutkijan tekemiin manuaalisiin analyyseihin sek\u00e4 olemassa olevaan puoliautomaattiseen menetelm\u00e4\u00e4n. Kehitetty menetelm\u00e4 suoriutui yleisesti hyvin verrattuna manuaaliseen ja puoliautomaattiseen analyysiin, ja toimi vakaasti eri ultra\u00e4\u00e4nilaitteilla ja asetuksilla otetuilla kuvilla. Menetelm\u00e4 kykeni my\u00f6s havaitsemaan monia lihassolukimppuja samassa kuvassa. Esitelty l\u00e4hestymistapa tarjoaa objektiivisen, aikaa s\u00e4\u00e4st\u00e4v\u00e4n menetelm\u00e4n ultra\u00e4\u00e4nikuvien segmentointiin ilman k\u00e4ytt\u00e4j\u00e4n sy\u00f6tett\u00e4. Menetelm\u00e4 ja kaikki merkitty data ovat saatavilla avoimen l\u00e4hdekoodin lisenssill\u00e4, mahdollistaen toisten hy\u00f6dynt\u00e4\u00e4 ja laajentaa t\u00e4t\u00e4 ty\u00f6t\u00e4.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Ultrasound is widely used to image musculoskeletal tissues, and offers many benefits including low cost, portability, and non-invasiveness. However, the analysis of ultrasound images remains an area in need of development. Until very recently, all analyses were performed manually, which is very subjective and time-consuming. There are currently very few open source methods designed for this purpose. Current approaches also tend to rely on rules-based analyses, and such methods tend to fail if images exhibit large deviations from those that were used to develop the method. In recent years, deep learning approaches to medical image analysis have been shown to yield excellent segmentation results on various imaging modalities, but such approaches have not yet been broadly applied to musculoskeletal ultrasound images. In this thesis I present a deep learning-based automated method that computes muscle architectural parameters from ultrasound images and videos. The method is based on the U-net architecture, and models were trained using hand-labelled images of muscle fascicles and aponeuroses. These trained models were then used to perform pixel-wise semantic segmentation of new images, classifying each pixel as one of three possible classes (fascicle, aponeurosis, other). The results were compared to manual analysis performed by two independent researchers, as well as to an existing semi-automated method. In general, the method performed very favourably when compared to manual and semi-automated analysis, and was robust to images from different muscles and those obtained with different ultrasound systems and settings. The method is also able to detect multiple muscle fascicles in a given image. The approach presented here offers an objective, time-efficient method of segmenting ultrasound images that does not require any user input. The method and all labelled training data are available under an open source license, allowing others to use and extend this work.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by Paivi Vuorio (paelvuor@jyu.fi) on 2020-04-15T05:08:55Z\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2020-04-15T05:08:55Z (GMT). No. of bitstreams: 0\n Previous issue date: 2020", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "56", "language": "", "element": "format", "qualifier": "extent", "schema": "dc"}, {"key": "dc.format.mimetype", "value": "application/pdf", "language": null, "element": "format", "qualifier": "mimetype", "schema": "dc"}, {"key": "dc.language.iso", "value": "eng", "language": null, "element": "language", "qualifier": "iso", "schema": "dc"}, {"key": "dc.rights", "value": "In Copyright", "language": "en", "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.subject.other", "value": "deep learning", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "U-net", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.title", "value": "Automated analysis of musculoskeletal ultrasound images using deep learning", "language": "", "element": "title", "qualifier": null, "schema": "dc"}, {"key": "dc.type", "value": "master thesis", "language": null, "element": "type", "qualifier": null, "schema": "dc"}, {"key": "dc.identifier.urn", "value": "URN:NBN:fi:jyu-202004152743", "language": "", "element": "identifier", "qualifier": "urn", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Pro gradu -tutkielma", "language": "fi", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Master\u2019s thesis", "language": "en", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Informaatioteknologian tiedekunta", "language": "fi", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Faculty of Information Technology", "language": "en", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Informaatioteknologia", "language": "fi", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Information Technology", "language": "en", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "Jyv\u00e4skyl\u00e4n yliopisto", "language": "fi", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "University of Jyv\u00e4skyl\u00e4", "language": "en", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Tietotekniikka", "language": "fi", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Mathematical Information Technology", "language": "en", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "yvv.contractresearch.funding", "value": "0", "language": "", "element": "contractresearch", "qualifier": "funding", "schema": "yvv"}, {"key": "dc.type.coar", "value": "http://purl.org/coar/resource_type/c_bdcc", "language": null, "element": "type", "qualifier": "coar", "schema": "dc"}, {"key": "dc.rights.accesslevel", "value": "openAccess", "language": null, "element": "rights", "qualifier": "accesslevel", "schema": "dc"}, {"key": "dc.type.publication", "value": "masterThesis", "language": null, "element": "type", "qualifier": "publication", "schema": "dc"}, {"key": "dc.subject.oppiainekoodi", "value": "602", "language": "", "element": "subject", "qualifier": "oppiainekoodi", "schema": "dc"}, {"key": "dc.subject.yso", "value": "kuvantaminen", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "ultra\u00e4\u00e4ni", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "lihakset", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "neuroverkot", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "segmentointi", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "imaging", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "ultrasound", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "muscles", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "neural networks", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "segmentation", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.format.content", "value": "fulltext", "language": null, "element": "format", "qualifier": "content", "schema": "dc"}, {"key": "dc.rights.url", "value": "https://rightsstatements.org/page/InC/1.0/", "language": null, "element": "rights", "qualifier": "url", "schema": "dc"}, {"key": "dc.type.okm", "value": "G2", "language": null, "element": "type", "qualifier": "okm", "schema": "dc"}]
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