用于3D牙齿分割的B样条嵌入结构学习
B-Spline Embedded Structure Learning for 3D Tooth Segmentation
AI总结：
针对真实牙列复杂导致3D牙齿分割困难的问题，提出B样条嵌入结构学习框架，引入SADC方法，在3DTeethSeg22基准上实现了新的SOTA精度，提升了复杂牙列处理能力。
AI中文摘要：
准确的3D牙齿分割是数字牙科的基础，但由于真实牙列的固有复杂性（如牙齿拥挤、错位、相邻牙齿间形态相似度高），它仍是一项艰巨挑战。为解决该问题，我们提出B样条嵌入结构学习，这是一种将牙齿的固有序列排列提炼为连续结构约束以正则化表示空间的新框架。我们的方法通过将参数化B样条轨迹拟合到牙齿中心来参数化全局牙列拓扑，为每个点分配连续结构嵌入，迫使共享主干网络捕捉全局牙弓结构。为充分利用这些嵌入先验，我们引入结构感知动态分类器（SADC），用自适应、按案例校准的决策边界替代刚性静态模板。SADC通过局部高斯邻近门正则化动态原型池化，并通过建模牙齿间空间关系和双侧对称性的注意力模块，使原型进行上下文协同演化。在3DTeethSeg22基准上的广泛评估表明，我们的方法建立了新的最先进精度，同时具有出色的结构鲁棒性和计算开销效率，显著增强了模型处理复杂牙列配置的能力。
英文摘要：
Accurate 3D tooth segmentation forms the cornerstone of digital dentistry, yet it remains a formidable challenge due to the inherent intricacy of real-world dentitions, such as crowding, misaligned teeth and high morphological similarity between adjacent teeth. To resolve this, we present B-Spline Embedded Structure Learning, a novel framework that distills the inherent sequential arrangement of teeth into a continuous structural constraint to regularize representation space. Our approach parameterizes the global dental topology by fitting a parametric B-spline trajectory to tooth centers, assigning each point a continuous structural embedding that forces the shared backbone to capture global arch organization. To fully exploit these embedded priors, we introduce a Structure-Aware Dynamic Classifier (SADC) to substitute rigid static templates with adaptive, case-calibrated decision boundaries. SADC regularizes dynamic prototype pooling via a localized Gaussian proximity gate and contextually co-evolves them through an attention block modeling spatial relations and bilateral symmetries across teeth. Extensive evaluations on the 3DTeethSeg22 benchmark demonstrate that our method establishes a new state-of-the-art accuracy with exceptional structural robustness and efficiency in computational overhead, markedly enhancing the model's capacity to handle complex dental configurations.
