B-Spline Embedded Structure Learning for 3D Tooth Segmentation

2026-08-17

AI Summary

为解决3D牙齿分割难题，提出B样条嵌入结构学习方法，通过参数化牙齿拓扑和结构感知动态分类器提高模型准确性与鲁棒性。

Abstract

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.

Problem

• 3D Tooth Segmentation
• Dental Topology
• Structural Constraint

Innovation

• B-Spline Embedded Structure Learning
• Structure-Aware Dynamic Classifier (SADC)
• Continuous Structural Constraint