{"slug": "b-spline-embedded-structure-learning-for-3d-tooth-segmentation", "title": "B-Spline Embedded Structure Learning for 3D Tooth Segmentation", "summary": "Researchers introduced B-Spline Embedded Structure Learning, a framework that improves 3D tooth segmentation by embedding dental arch structure into the model. On the 3DTeethSeg22 benchmark, the method achieves state-of-the-art accuracy with reduced computational overhead, addressing challenges like crowding and morphological similarity.", "body_md": "arXiv:2608.17291v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/b-spline-embedded-structure-learning-for-3d-tooth-segmentation", "canonical_source": "https://arxiv.org/abs/2608.17291", "published_at": "2026-08-19 04:00:00+00:00", "updated_at": "2026-08-19 04:13:03.791607+00:00", "lang": "en", "topics": ["machine-learning", "computer-vision"], "entities": ["3DTeethSeg22"], "alternates": {"html": "https://wpnews.pro/news/b-spline-embedded-structure-learning-for-3d-tooth-segmentation", "markdown": "https://wpnews.pro/news/b-spline-embedded-structure-learning-for-3d-tooth-segmentation.md", "text": "https://wpnews.pro/news/b-spline-embedded-structure-learning-for-3d-tooth-segmentation.txt", "jsonld": "https://wpnews.pro/news/b-spline-embedded-structure-learning-for-3d-tooth-segmentation.jsonld"}}