Accurate three-dimensional (3D) tooth segmentation from Cone-Beam Computed Tomography (CBCT) is a prerequisite for digital dental workflows. However, achieving high-fidelity segmentation remains challenging due to adhesion artifacts in naturally occluded scans, which are caused by low contrast and indistinct inter-arch boundaries. To address these limitations, we propose the Anatomy Aware Cascade Network (AACNet), a coarse-to-fine framework designed to resolve boundary ambiguity while maintaining global structural consistency. Specifically, we introduce two mechanisms: the Ambiguity Gated Boundary Refiner (AGBR) and the Signed Distance Map guided Anatomical Attention (SDMAA). The AGBR employs an entropy based gating mechanism to perform targeted feature rectification in high uncertainty transition zones. Meanwhile, the SDMAA integrates implicit geometric constraints via signed distance map to enforce topological consistency, preventing the loss of spatial details associated with standard pooling. Experimental results on a dataset of 125 CBCT volumes demonstrate that AACNet achieves a Dice Similarity Coefficient of 90.17 \% and a 95\% Hausdorff Distance of 3.63 mm, significantly outperforming state-of-the-art methods. Furthermore, the model exhibits strong generalization on an external dataset with an HD95 of 2.19 mm, validating its reliability for downstream clinical applications such as surgical planning. Code for AACNet is available at https://github.com/shiliu0114/AACNet.

翻译：从锥形束计算机断层扫描（CBCT）中实现精确的三维牙齿分割是数字化牙科工作流程的前提条件。然而，由于自然咬合扫描中低对比度和牙弓间边界模糊导致的粘连伪影，实现高保真分割仍具挑战性。为克服这些局限，我们提出解剖感知级联网络（AACNet），一种从粗到精的框架，旨在解决边界模糊性的同时保持全局结构一致性。具体而言，我们引入两种机制：模糊门控边界细化器（AGBR）与符号距离图引导的解剖注意力（SDMAA）。AGBR采用基于熵的门控机制，在高不确定性过渡区域执行针对性特征校正。同时，SDMAA通过符号距离图整合隐式几何约束以强化拓扑一致性，避免标准池化操作导致的空间细节丢失。在包含125个CBCT扫描数据集的实验结果表明，AACNet实现了90.17%的戴斯相似系数与3.63毫米的95%豪斯多夫距离，显著优于现有先进方法。此外，该模型在外部数据集上表现出2.19毫米HD95的强泛化能力，验证了其在外科手术规划等下游临床应用中的可靠性。AACNet代码发布于https://github.com/shiliu0114/AACNet。