3D Auto-Segmentation of Mandibular Condyles

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniv Michigan-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorUniv N Carolina-
Autor(es): dc.creatorBrosset, Serge-
Autor(es): dc.creatorDumont, Maxime-
Autor(es): dc.creatorBianchi, Jonas [UNESP]-
Autor(es): dc.creatorRuellas, Antonio-
Autor(es): dc.creatorCevidanes, Lucia-
Autor(es): dc.creatorYatabe, Marilia-
Autor(es): dc.creatorGoncalves, Joao [UNESP]-
Autor(es): dc.creatorBenavides, Erika-
Autor(es): dc.creatorSoki, Fabiana-
Autor(es): dc.creatorPaniagua, Beatriz-
Autor(es): dc.creatorPrieto, Juan-
Autor(es): dc.creatorNajarian, Kayvan-
Autor(es): dc.creatorGryak, Jonathan-
Autor(es): dc.creatorSoroushmehr, Reza-
Autor(es): dc.creatorIEEE-
Data de aceite: dc.date.accessioned2022-02-22T00:55:44Z-
Data de disponibilização: dc.date.available2022-02-22T00:55:44Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2019-12-31-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/209230-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/209230-
Descrição: dc.descriptionTemporomandibular joints (TMJ) like a hinge connect the jawbone to the skull. TMJ disorders could cause pain in the jaw joint and the muscles controlling jaw movement. However, the disease cannot be diagnosed until it becomes symptomatic. It has been shown that bone resorption at the condyle articular surface is already evident at initial diagnosis of TMJ Osteoarthritis (OA). Therefore, analyzing the bone structure will facilitate the disease diagnosis. The important step towards this analysis is the condyle segmentation. This article deals with a method to automatically segment the temporomandibular joint condyle out of cone beam CT (CBCT) scans. In the proposed method we denoise images and apply 3D active contour and morphological operations to segment the condyle. The experimental results show that the proposed method yields the Dice score of 0.9461 with the standards deviation of 0.0888 when it is applied on CBCT images of 95 patients. This segmentation will allow large datasets to be analyzed more efficiently towards data sciences and machine learning approaches for disease classification.-
Descrição: dc.descriptionNIDCR-
Descrição: dc.descriptionUniv Michigan, Dept Orthodont & Pediat Dent, Ann Arbor, MI 48109 USA-
Descrição: dc.descriptionSao Paulo State Univ, Pediat Dent & Orthodont, Sao Paulo, Brazil-
Descrição: dc.descriptionUniv Michigan, Dept Periodont & Oral Med, Ann Arbor, MI 48109 USA-
Descrição: dc.descriptionUniv Michigan, Dept Computat Med & Bioinformat, Ann Arbor, MI 48109 USA-
Descrição: dc.descriptionUniv N Carolina, Psychiat, Chapel Hill, NC 27515 USA-
Descrição: dc.descriptionUniv N Carolina, Dept Psychiat, Chapel Hill, NC 27515 USA-
Descrição: dc.descriptionUniv N Carolina, Dept Orthodont, Chapel Hill, NC 27515 USA-
Descrição: dc.descriptionUniv N Carolina, Dept Comp Sci, Chapel Hill, NC 27515 USA-
Descrição: dc.descriptionSao Paulo State Univ, Pediat Dent & Orthodont, Sao Paulo, Brazil-
Descrição: dc.descriptionNIDCR: DEO24450-
Formato: dc.format1270-1273-
Idioma: dc.languageen-
Publicador: dc.publisherIeee-
Relação: dc.relation42nd Annual International Conferences Of The Ieee Engineering In Medicine And Biology Society: Enabling Innovative Technologies For Global Healthcare Embc'20-
???dc.source???: dc.sourceWeb of Science-
Título: dc.title3D Auto-Segmentation of Mandibular Condyles-
Aparece nas coleções:Repositório Institucional - Unesp

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