Tooth segmentation on multimodal images using adapted segment anything model
Abstract With the increase in dental patient numbers and the ongoing digital transformation of dental hospitals, tooth segmentation has become increasingly crucial for the digital diagnosis, design, treatment, and customized appliance manufacturing of orthodontics, oral implant surgery, and prosthod...
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| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-96301-2 |
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| author | Peijuan Wang Hanjie Gu Yuliang Sun |
| author_facet | Peijuan Wang Hanjie Gu Yuliang Sun |
| author_sort | Peijuan Wang |
| collection | DOAJ |
| description | Abstract With the increase in dental patient numbers and the ongoing digital transformation of dental hospitals, tooth segmentation has become increasingly crucial for the digital diagnosis, design, treatment, and customized appliance manufacturing of orthodontics, oral implant surgery, and prosthodontics. This study aims to adapt the Segment Anything Model (SAM) to the Tooth segmentation task for precise tooth segmentation performance. In this study, a novel tooth segmentation method-Tooth-ASAM-that harnesses the power of SAM was introduced. An adapter-based image encoder and mask decoder specifically tailored for adapting SAM to tooth images were designed. The proposed method was evaluated through rigorous evaluation of multimodal tooth images-including Cone Beam Computed Tomography (CBCT) images, panoramic X-rays, and natural teeth images captured by a micro-camera. The experimental results unequivocally show that Tooth-ASAM achieved remarkable performances across all four datasets, excelling in key metrics like the Dice coefficient, IoU, HD95, and ASSD. Furthermore, the proposed Tooth-ASAM delivered perceptually more accurate segmentation results than the state-of-the-art methods on the four tooth datasets. This research demonstrates that precise tooth segmentation performances were obtained by applying SAM and adaptation training strategy, making it highly suitable for clinical applications in orthodontics, oral implant surgery, and prosthodontics. |
| format | Article |
| id | doaj-art-e8ca1d707ae74ef3abff0dd6232a3ef7 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e8ca1d707ae74ef3abff0dd6232a3ef72025-08-20T03:14:05ZengNature PortfolioScientific Reports2045-23222025-04-0115111310.1038/s41598-025-96301-2Tooth segmentation on multimodal images using adapted segment anything modelPeijuan Wang0Hanjie Gu1Yuliang Sun2College of Information Science and Technology, Zhejiang Shuren UniversityCollege of Information Science and Technology, Zhejiang Shuren UniversityCollege of Information Science and Technology, Zhejiang Shuren UniversityAbstract With the increase in dental patient numbers and the ongoing digital transformation of dental hospitals, tooth segmentation has become increasingly crucial for the digital diagnosis, design, treatment, and customized appliance manufacturing of orthodontics, oral implant surgery, and prosthodontics. This study aims to adapt the Segment Anything Model (SAM) to the Tooth segmentation task for precise tooth segmentation performance. In this study, a novel tooth segmentation method-Tooth-ASAM-that harnesses the power of SAM was introduced. An adapter-based image encoder and mask decoder specifically tailored for adapting SAM to tooth images were designed. The proposed method was evaluated through rigorous evaluation of multimodal tooth images-including Cone Beam Computed Tomography (CBCT) images, panoramic X-rays, and natural teeth images captured by a micro-camera. The experimental results unequivocally show that Tooth-ASAM achieved remarkable performances across all four datasets, excelling in key metrics like the Dice coefficient, IoU, HD95, and ASSD. Furthermore, the proposed Tooth-ASAM delivered perceptually more accurate segmentation results than the state-of-the-art methods on the four tooth datasets. This research demonstrates that precise tooth segmentation performances were obtained by applying SAM and adaptation training strategy, making it highly suitable for clinical applications in orthodontics, oral implant surgery, and prosthodontics.https://doi.org/10.1038/s41598-025-96301-2Tooth segmentationSegment Anything ModelMultimodal imagesDentistry |
| spellingShingle | Peijuan Wang Hanjie Gu Yuliang Sun Tooth segmentation on multimodal images using adapted segment anything model Scientific Reports Tooth segmentation Segment Anything Model Multimodal images Dentistry |
| title | Tooth segmentation on multimodal images using adapted segment anything model |
| title_full | Tooth segmentation on multimodal images using adapted segment anything model |
| title_fullStr | Tooth segmentation on multimodal images using adapted segment anything model |
| title_full_unstemmed | Tooth segmentation on multimodal images using adapted segment anything model |
| title_short | Tooth segmentation on multimodal images using adapted segment anything model |
| title_sort | tooth segmentation on multimodal images using adapted segment anything model |
| topic | Tooth segmentation Segment Anything Model Multimodal images Dentistry |
| url | https://doi.org/10.1038/s41598-025-96301-2 |
| work_keys_str_mv | AT peijuanwang toothsegmentationonmultimodalimagesusingadaptedsegmentanythingmodel AT hanjiegu toothsegmentationonmultimodalimagesusingadaptedsegmentanythingmodel AT yuliangsun toothsegmentationonmultimodalimagesusingadaptedsegmentanythingmodel |