3D Auto-Segmentation of Mandibular Condyles

• Autor(a) principal: Brosset, Serge
• Data de Publicação: 2020
• Outros Autores: Dumont, Maxime, Bianchi, Jonas [UNESP], Ruellas, Antonio, Cevidanes, Lucia, Yatabe, Marilia, Goncalves, Joao [UNESP], Benavides, Erika, Soki, Fabiana, Paniagua, Beatriz, Prieto, Juan, Najarian, Kayvan, Gryak, Jonathan, Soroushmehr, Reza
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/EMBC44109.2020.9175692; http://hdl.handle.net/11449/221555

Temporomandibular 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.