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arXiv · 2511.16936

Shape-preserving Tooth Segmentation from CBCT Images Using Deep Learning with Semantic and Shape Awareness

Zongrui Ji·Zhiming Cui·Na Li·Qianhan Zheng·Miaojing Shi·Ke Deng·Jingyang Zhang·Chaoyuan Li·Xuepeng Chen·Yi Dong·Lei Ma
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Abstract

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.

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Zongrui Ji, Zhiming Cui, Na Li, Qianhan Zheng, Miaojing Shi, Ke Deng, Jingyang Zhang, Chaoyuan Li, Xuepeng Chen, Yi Dong, Lei Ma. 2025-11-21. Shape-preserving Tooth Segmentation from CBCT Images Using Deep Learning with Semantic and Shape Awareness. https://arxiv.org/abs/2511.16936

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