Mandible and skull segmentation in cone beam computed tomography using super-voxels and graph clustering
| Autor: | Jonas Bianchi, Bernd Hamann, Dirceu Barnabé Raveli, João do Espirito Santo Batista Neto, Oscar Cuadros Linares |
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| Přispěvatelé: | Universidade de São Paulo (USP), Universidade Estadual Paulista (Unesp), University of California |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Cone beam computed tomography
Computer science Image processing 02 engineering and technology Mandible computer.software_genre TOMOGRAFIA 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine stomatognathic system Graph clustering Voxel Hounsfield scale 0202 electrical engineering electronic engineering information engineering Medical imaging medicine Segmentation Computer vision business.industry 3D reconstruction Skull Bone segmentation Super-voxels 020207 software engineering Computer Graphics and Computer-Aided Design medicine.anatomical_structure Computer Vision and Pattern Recognition Artificial intelligence business computer Software |
| Zdroj: | Scopus Repositório Institucional da UNESP Universidade Estadual Paulista (UNESP) instacron:UNESP Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual) Universidade de São Paulo (USP) instacron:USP |
| Popis: | Made available in DSpace on 2018-12-11T16:52:59Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-04-26 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. Instituto de Ciências Matemáticas e de Computação (ICMC) University of São Paulo (USP) Faculdade de Odontologia (FOAR) São Paulo State University (UNESP) Department of Computer Science University of California Faculdade de Odontologia (FOAR) São Paulo State University (UNESP) |
| Databáze: | OpenAIRE |
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