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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.16936 |
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| _version_ | 1866914166141353984 |
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| author | Ji, Zongrui Cui, Zhiming Li, Na Zheng, Qianhan Shi, Miaojing Deng, Ke Zhang, Jingyang Li, Chaoyuan Chen, Xuepeng Dong, Yi Ma, Lei |
| author_facet | Ji, Zongrui Cui, Zhiming Li, Na Zheng, Qianhan Shi, Miaojing Deng, Ke Zhang, Jingyang Li, Chaoyuan Chen, Xuepeng Dong, Yi Ma, Lei |
| contents | Background:Accurate tooth segmentation from cone beam computed tomography (CBCT) images is crucial for digital dentistry but remains challenging in cases of interdental adhesions, which cause severe anatomical shape distortion.
Methods:
To address this, we propose a deep learning framework that integrates semantic and shape awareness for shape-preserving segmentation. Our method introduces a target-tooth-centroid prompted multi-label learning strategy to model semantic relationships between teeth, reducing shape ambiguity. Additionally, a tooth-shape-aware learning mechanism explicitly enforces morphological constraints to preserve boundary integrity. These components are unified via multi-task learning, jointly optimizing segmentation and shape preservation.
Results: Extensive evaluations on internal and external datasets demonstrate that our approach significantly outperforms existing methods.
Conclusions: Our approach effectively mitigates shape distortions and providing anatomically faithful tooth boundaries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16936 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Shape-preserving Tooth Segmentation from CBCT Images Using Deep Learning with Semantic and Shape Awareness Ji, Zongrui Cui, Zhiming Li, Na Zheng, Qianhan Shi, Miaojing Deng, Ke Zhang, Jingyang Li, Chaoyuan Chen, Xuepeng Dong, Yi Ma, Lei Computer Vision and Pattern Recognition Background:Accurate tooth segmentation from cone beam computed tomography (CBCT) images is crucial for digital dentistry but remains challenging in cases of interdental adhesions, which cause severe anatomical shape distortion. Methods: To address this, we propose a deep learning framework that integrates semantic and shape awareness for shape-preserving segmentation. Our method introduces a target-tooth-centroid prompted multi-label learning strategy to model semantic relationships between teeth, reducing shape ambiguity. Additionally, a tooth-shape-aware learning mechanism explicitly enforces morphological constraints to preserve boundary integrity. These components are unified via multi-task learning, jointly optimizing segmentation and shape preservation. Results: Extensive evaluations on internal and external datasets demonstrate that our approach significantly outperforms existing methods. Conclusions: Our approach effectively mitigates shape distortions and providing anatomically faithful tooth boundaries. |
| title | Shape-preserving Tooth Segmentation from CBCT Images Using Deep Learning with Semantic and Shape Awareness |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.16936 |