B-Spline Embedded Structure Learning for 3D Tooth Segmentation

• URL: http://arxiv.org/abs/2608.17291v1
• Date: Tue, 18 Aug 2026 02:43:49 GMT
• Title: B-Spline Embedded Structure Learning for 3D Tooth Segmentation
• Abstract summary: Bline Embedded Structure Learning is a novel framework that distills the inherent sequential arrangement of teeth into a continuous structural constraint.<n>Our method establishes a new state-of-the-art accuracy with exceptional structural robustness and efficiency in computational overhead.
• Score: 4.713922533924589
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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