Jawbone Segmentation with Trabecular Bone Preservation from Cone Beam CT Images
To segment jaw structures in both open and closed mouth states from cone beam CT images and to preserve the true structures of trabecular bones. Segmentation algorithms of the mandible and maxilla were designed based on their different structures. We detected edges in volume and segment edges of the...
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| Format: | Article |
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MDPI AG
2022-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/3/1556 |
| _version_ | 1811153732625235968 |
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| author | Songze Zhang Benxiang Jiang Hongjian Shi |
| author_facet | Songze Zhang Benxiang Jiang Hongjian Shi |
| author_sort | Songze Zhang |
| collection | DOAJ |
| description | To segment jaw structures in both open and closed mouth states from cone beam CT images and to preserve the true structures of trabecular bones. Segmentation algorithms of the mandible and maxilla were designed based on their different structures. We detected edges in volume and segment edges of the mandible and maxilla whether the mouth of a patient was open or closed. The internal structures of the mandible and maxilla were preserved by a morphological method with different parameters, respectively. An axial plane corresponding to the bottom of the hard palate was identified so that the bone structures of the maxilla above this plane were removed. Finally, the mandibular surface was smoothed by a simple thresholding method, and the maxillary surface was optimized by a geodesic active contours method. The 3D jaw model was segmented automatically by our proposed procedure with high accuracy. The average dice coefficients of the mandible and maxilla were equal to 0.9709 and 0.9420, respectively. The proposed jaw segmentation algorithm is automatic and knowledge-driven. The segmented jaws truly demonstrate the cortical and trabecular bone structures. It may potentially assist doctors in diagnosis and surgical planning. |
| first_indexed | 2024-03-10T00:11:03Z |
| format | Article |
| id | doaj.art-507a1a6d56784070a576804e519febcf |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T00:11:03Z |
| publishDate | 2022-01-01 |
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| spelling | doaj.art-507a1a6d56784070a576804e519febcf2023-11-23T15:59:34ZengMDPI AGApplied Sciences2076-34172022-01-01123155610.3390/app12031556Jawbone Segmentation with Trabecular Bone Preservation from Cone Beam CT ImagesSongze Zhang0Benxiang Jiang1Hongjian Shi2Division of Science and Technology, Beijing Normal University—Hong Kong Baptist University United International College, Division of Science and Technology, Zhuhai 519087, ChinaDivision of Science and Technology, Beijing Normal University—Hong Kong Baptist University United International College, Division of Science and Technology, Zhuhai 519087, ChinaDivision of Science and Technology, Beijing Normal University—Hong Kong Baptist University United International College, Division of Science and Technology, Zhuhai 519087, ChinaTo segment jaw structures in both open and closed mouth states from cone beam CT images and to preserve the true structures of trabecular bones. Segmentation algorithms of the mandible and maxilla were designed based on their different structures. We detected edges in volume and segment edges of the mandible and maxilla whether the mouth of a patient was open or closed. The internal structures of the mandible and maxilla were preserved by a morphological method with different parameters, respectively. An axial plane corresponding to the bottom of the hard palate was identified so that the bone structures of the maxilla above this plane were removed. Finally, the mandibular surface was smoothed by a simple thresholding method, and the maxillary surface was optimized by a geodesic active contours method. The 3D jaw model was segmented automatically by our proposed procedure with high accuracy. The average dice coefficients of the mandible and maxilla were equal to 0.9709 and 0.9420, respectively. The proposed jaw segmentation algorithm is automatic and knowledge-driven. The segmented jaws truly demonstrate the cortical and trabecular bone structures. It may potentially assist doctors in diagnosis and surgical planning.https://www.mdpi.com/2076-3417/12/3/1556trabecular boneCBCT imagesmandiblemaxillaregion growing |
| spellingShingle | Songze Zhang Benxiang Jiang Hongjian Shi Jawbone Segmentation with Trabecular Bone Preservation from Cone Beam CT Images Applied Sciences trabecular bone CBCT images mandible maxilla region growing |
| title | Jawbone Segmentation with Trabecular Bone Preservation from Cone Beam CT Images |
| title_full | Jawbone Segmentation with Trabecular Bone Preservation from Cone Beam CT Images |
| title_fullStr | Jawbone Segmentation with Trabecular Bone Preservation from Cone Beam CT Images |
| title_full_unstemmed | Jawbone Segmentation with Trabecular Bone Preservation from Cone Beam CT Images |
| title_short | Jawbone Segmentation with Trabecular Bone Preservation from Cone Beam CT Images |
| title_sort | jawbone segmentation with trabecular bone preservation from cone beam ct images |
| topic | trabecular bone CBCT images mandible maxilla region growing |
| url | https://www.mdpi.com/2076-3417/12/3/1556 |
| work_keys_str_mv | AT songzezhang jawbonesegmentationwithtrabecularbonepreservationfromconebeamctimages AT benxiangjiang jawbonesegmentationwithtrabecularbonepreservationfromconebeamctimages AT hongjianshi jawbonesegmentationwithtrabecularbonepreservationfromconebeamctimages |