Semi or fully automatic tooth segmentation in CBCT images: a review
Cone beam computed tomography (CBCT) is widely employed in modern dentistry, and tooth segmentation constitutes an integral part of the digital workflow based on these imaging data. Previous methodologies rely heavily on manual segmentation and are time-consuming and labor-intensive in clinical prac...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2024-04-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-1994.pdf |
| _version_ | 1797199001439698944 |
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| author | Qianhan Zheng Yu Gao Mengqi Zhou Huimin Li Jiaqi Lin Weifang Zhang Xuepeng Chen |
| author_facet | Qianhan Zheng Yu Gao Mengqi Zhou Huimin Li Jiaqi Lin Weifang Zhang Xuepeng Chen |
| author_sort | Qianhan Zheng |
| collection | DOAJ |
| description | Cone beam computed tomography (CBCT) is widely employed in modern dentistry, and tooth segmentation constitutes an integral part of the digital workflow based on these imaging data. Previous methodologies rely heavily on manual segmentation and are time-consuming and labor-intensive in clinical practice. Recently, with advancements in computer vision technology, scholars have conducted in-depth research, proposing various fast and accurate tooth segmentation methods. In this review, we review 55 articles in this field and discuss the effectiveness, advantages, and disadvantages of each approach. In addition to simple classification and discussion, this review aims to reveal how tooth segmentation methods can be improved by the application and refinement of existing image segmentation algorithms to solve problems such as irregular morphology and fuzzy boundaries of teeth. It is assumed that with the optimization of these methods, manual operation will be reduced, and greater accuracy and robustness in tooth segmentation will be achieved. Finally, we highlight the challenges that still exist in this field and provide prospects for future directions. |
| first_indexed | 2024-04-24T07:08:49Z |
| format | Article |
| id | doaj.art-99e94a76e4db4f67863b01dd55942115 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-04-24T07:08:49Z |
| publishDate | 2024-04-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-99e94a76e4db4f67863b01dd559421152024-04-21T15:05:19ZengPeerJ Inc.PeerJ Computer Science2376-59922024-04-0110e199410.7717/peerj-cs.1994Semi or fully automatic tooth segmentation in CBCT images: a reviewQianhan Zheng0Yu Gao1Mengqi Zhou2Huimin Li3Jiaqi Lin4Weifang Zhang5Xuepeng Chen6Stomatology Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaStomatology Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaStomatology Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaStomatology Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaStomatology Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaStomatology Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaStomatology Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaCone beam computed tomography (CBCT) is widely employed in modern dentistry, and tooth segmentation constitutes an integral part of the digital workflow based on these imaging data. Previous methodologies rely heavily on manual segmentation and are time-consuming and labor-intensive in clinical practice. Recently, with advancements in computer vision technology, scholars have conducted in-depth research, proposing various fast and accurate tooth segmentation methods. In this review, we review 55 articles in this field and discuss the effectiveness, advantages, and disadvantages of each approach. In addition to simple classification and discussion, this review aims to reveal how tooth segmentation methods can be improved by the application and refinement of existing image segmentation algorithms to solve problems such as irregular morphology and fuzzy boundaries of teeth. It is assumed that with the optimization of these methods, manual operation will be reduced, and greater accuracy and robustness in tooth segmentation will be achieved. Finally, we highlight the challenges that still exist in this field and provide prospects for future directions.https://peerj.com/articles/cs-1994.pdfCBCTTooth segmentationLevel setDeep learningUNet |
| spellingShingle | Qianhan Zheng Yu Gao Mengqi Zhou Huimin Li Jiaqi Lin Weifang Zhang Xuepeng Chen Semi or fully automatic tooth segmentation in CBCT images: a review PeerJ Computer Science CBCT Tooth segmentation Level set Deep learning UNet |
| title | Semi or fully automatic tooth segmentation in CBCT images: a review |
| title_full | Semi or fully automatic tooth segmentation in CBCT images: a review |
| title_fullStr | Semi or fully automatic tooth segmentation in CBCT images: a review |
| title_full_unstemmed | Semi or fully automatic tooth segmentation in CBCT images: a review |
| title_short | Semi or fully automatic tooth segmentation in CBCT images: a review |
| title_sort | semi or fully automatic tooth segmentation in cbct images a review |
| topic | CBCT Tooth segmentation Level set Deep learning UNet |
| url | https://peerj.com/articles/cs-1994.pdf |
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