Mandible and skull segmentation in cone beam computed tomography using super-voxels and graph clustering

Autor(es)

Linares, Oscar Cuadros ^[1] ; Bianchi, Jonas ^[2] ; Raveli, Dirceu ^[2] ; Batista Neto, Joao ^[1] ; Hamann, Bernd ^[3]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, ICMC, BR-13566590 Sao Carlos, SP - Brazil

^[2] Sao Paulo State Univ UNESP, Fac Odontol FOAR, BR-14801385 Araraquara, SP - Brazil

^[3] Univ Calif Davis, Dept Comp Sci, Davis, CA 95616 - USA

• Tipo de documento: Artigo Científico
• Fonte: VISUAL COMPUTER; v. 35, n. 10, p. 1461-1474, OCT 2019.
• Citações Web of Science: 0

Resumo

Cone beam computed tomography (CBCT) is a medical imaging technique employed for diagnosis and treatment of patients with cranio-maxillofacial deformities. CBCT 3D reconstruction and segmentation of bones such as mandible or maxilla are essential procedures in surgical and orthodontic treatments. However, CBCT image processing may be impaired by features such as low contrast, inhomogeneity, noise and artifacts. Besides, values assigned to voxels are relative Hounsfield units unlike traditional computed tomography (CT). Such drawbacks render CBCT segmentation a difficult and time-consuming task, usually performed manually with tools designed for medical image processing. We present an interactive two-stage method for the segmentation of CBCT: (i) we first perform an automatic segmentation of bone structures with super-voxels, allowing a compact graph representation of the 3D data; (ii) next, a user-placed seed process guides a graph partitioning algorithm, splitting the extracted bones into mandible and skull. We have evaluated our segmentation method in three different scenarios and compared the results with ground truth data of the mandible and the skull. Results show that our method produces accurate segmentation and is robust to changes in parameters. We also compared our method with two similar segmentation strategy and showed that it produces more accurate segmentation. Finally, we evaluated our method for CT data of patients with deformed or missing bones and the segmentation was accurate for all data. The segmentation of a typical CBCT takes in average 5 min, which is faster than most techniques currently available. (AU)

• Processo FAPESP: 12/24036-1 - Segmentação Tri Dimensional do Crânio para o monitoramento de alterações ósseas aplicada à Odontologia
• Beneficiário: Oscar Alonso Cuadros Linares
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 15/12771-7 - Segmentação tridimensional do crânio para monitoramento de alterações ósseas em odontologia
• Beneficiário: Oscar Alonso Cuadros Linares
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Doutorado