3D Auto-Segmentation of Mandibular Condyles

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Autor(es): dc.contributorUniversity of Michigan-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorOrthodontics and Computer Science-
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-
Data de aceite: dc.date.accessioned2022-08-04T22:08:55Z-
Data de disponibilização: dc.date.available2022-08-04T22:08:55Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2020-07-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/EMBC44109.2020.9175692-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/221555-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/221555-
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.descriptionUniversity of Michigan Department of Orthodontics and Pediatric Dentistry-
Descrição: dc.descriptionPediatric Dentistry and Orthodontics São Paulo State University-
Descrição: dc.descriptionUniversity of Michigan Department of Periodontics and Oral Medicine-
Descrição: dc.descriptionUniversity of Michigan Department of Computational Medicine and Bioinformatics-
Descrição: dc.descriptionUniversity of North Carolina Departments of Psychiatry Orthodontics and Computer Science-
Descrição: dc.descriptionPediatric Dentistry and Orthodontics São Paulo State University-
Formato: dc.format1270-1273-
Idioma: dc.languageen-
Relação: dc.relationProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS-
???dc.source???: dc.sourceScopus-
Título: dc.title3D Auto-Segmentation of Mandibular Condyles-
Aparece nas coleções:Repositório Institucional - Unesp

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