Accurate malocclusion tooth segmentation method based on a level set with adaptive edge feature enhancement
Objective: This study aimed to accurately segment teeth under complex oral conditions, including complex structural interference among adjacent teeth or malocclusion conditions, such as tooth rotation and displacement caused by dental crowding. Study design: Cone-beam computed tomography (CBCT) imag...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023108504 |
| _version_ | 1797336987959558144 |
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| author | Shuyi Jiang Han Zhang Zhi Mao Yonghui Li Guanyuan Feng |
| author_facet | Shuyi Jiang Han Zhang Zhi Mao Yonghui Li Guanyuan Feng |
| author_sort | Shuyi Jiang |
| collection | DOAJ |
| description | Objective: This study aimed to accurately segment teeth under complex oral conditions, including complex structural interference among adjacent teeth or malocclusion conditions, such as tooth rotation and displacement caused by dental crowding. Study design: Cone-beam computed tomography (CBCT) images were obtained from 19 patients with complex oral conditions, and a three-step solution was proposed. This study used a global convex level-set model to extract bony tissue and developed a flexible curve extraction method for separating neighbouring teeth under complex structural interference. In addition, a local level-set model with adaptive edge feature enhancement was proposed to segment individual teeth precisely. This model adaptively enhances edge features based on the structure of the root boundary and accurately distinguishes between the close-contact root and alveolar bone resulting from tooth rotation or displacement. Results: The experimental results showed that the average Dice similarity coefficient values for incisors, canines, premolars, and molars were 93.30%, 93.47%, 93.24%, and 93.89%, respectively, and the average tooth centroid distances were 0.66, 0.61, 0.87, and 0.80 mm, respectively. Conclusion: The proposed method can effectively segment teeth without relying on highly precise annotated datasets, yielding satisfactory results even under complex structural interference between adjacent teeth or tooth rotation and displacement caused by dental crowding. It is more robust than the other methods and provides valuable data for further research and clinical practice. |
| first_indexed | 2024-03-08T09:02:52Z |
| format | Article |
| id | doaj.art-cbf3be6bf5324c0ba7d338f0e958e424 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-08T09:02:52Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-cbf3be6bf5324c0ba7d338f0e958e4242024-02-01T06:32:44ZengElsevierHeliyon2405-84402024-01-01101e23642Accurate malocclusion tooth segmentation method based on a level set with adaptive edge feature enhancementShuyi Jiang0Han Zhang1Zhi Mao2Yonghui Li3Guanyuan Feng4College of Computer Science and Technology, Changchun University of Science and Technology, Changchun, 130012, ChinaDepartment of Orthodontics, Jilin University Stomatology Hospital, Changchun, 130021, ChinaDepartment of Orthodontics, Jilin University Stomatology Hospital, Changchun, 130021, ChinaCollege of Computer Science and Technology, Changchun University of Science and Technology, Changchun, 130012, ChinaCollege of Computer Science and Technology, Changchun University of Science and Technology, Changchun, 130012, China; Corresponding author.Objective: This study aimed to accurately segment teeth under complex oral conditions, including complex structural interference among adjacent teeth or malocclusion conditions, such as tooth rotation and displacement caused by dental crowding. Study design: Cone-beam computed tomography (CBCT) images were obtained from 19 patients with complex oral conditions, and a three-step solution was proposed. This study used a global convex level-set model to extract bony tissue and developed a flexible curve extraction method for separating neighbouring teeth under complex structural interference. In addition, a local level-set model with adaptive edge feature enhancement was proposed to segment individual teeth precisely. This model adaptively enhances edge features based on the structure of the root boundary and accurately distinguishes between the close-contact root and alveolar bone resulting from tooth rotation or displacement. Results: The experimental results showed that the average Dice similarity coefficient values for incisors, canines, premolars, and molars were 93.30%, 93.47%, 93.24%, and 93.89%, respectively, and the average tooth centroid distances were 0.66, 0.61, 0.87, and 0.80 mm, respectively. Conclusion: The proposed method can effectively segment teeth without relying on highly precise annotated datasets, yielding satisfactory results even under complex structural interference between adjacent teeth or tooth rotation and displacement caused by dental crowding. It is more robust than the other methods and provides valuable data for further research and clinical practice.http://www.sciencedirect.com/science/article/pii/S2405844023108504Dental CBCT imagesOrthodonticsImage segmentationDental crowdingTooth deformity |
| spellingShingle | Shuyi Jiang Han Zhang Zhi Mao Yonghui Li Guanyuan Feng Accurate malocclusion tooth segmentation method based on a level set with adaptive edge feature enhancement Heliyon Dental CBCT images Orthodontics Image segmentation Dental crowding Tooth deformity |
| title | Accurate malocclusion tooth segmentation method based on a level set with adaptive edge feature enhancement |
| title_full | Accurate malocclusion tooth segmentation method based on a level set with adaptive edge feature enhancement |
| title_fullStr | Accurate malocclusion tooth segmentation method based on a level set with adaptive edge feature enhancement |
| title_full_unstemmed | Accurate malocclusion tooth segmentation method based on a level set with adaptive edge feature enhancement |
| title_short | Accurate malocclusion tooth segmentation method based on a level set with adaptive edge feature enhancement |
| title_sort | accurate malocclusion tooth segmentation method based on a level set with adaptive edge feature enhancement |
| topic | Dental CBCT images Orthodontics Image segmentation Dental crowding Tooth deformity |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023108504 |
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