用深度学习精准分割牙科CT中的牙齿，保留真实形态。
Shape-preserving Tooth Segmentation from CBCT Images Using Deep Learning with Semantic and Shape Awareness
- 引入语义与形状感知机制，减少牙齿间形态混淆
- 在多个数据集上分割精度显著优于现有方法
- 适合需要高保真牙齿模型的数字牙科应用
背景：从锥形束计算机断层扫描（CBCT）图像中准确分割牙齿对数字牙科至关重要，但在邻牙粘连情况下，解剖形态严重扭曲，分割难度大。方法：为此，我们提出一种融合语义与形状感知的深度学习框架，实现形态保持的分割。方法引入基于目标牙中心提示的多标签学习策略，建模牙齿间的语义关系，降低形状歧义；同时采用牙齿形状感知学习机制，显式施加形态约束，保障边界完整性。二者通过多任务学习统一优化，联合提升分割与形态保持效果。结果：在内部和外部数据集上的大量评估表明，该方法显著优于现有方法。结论：本方法有效缓解形态畸变，提供解剖学上真实的牙齿边界。
原文摘要 · Abstract (English)
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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