Silhouette-to-Contour Registration: Aligning Intraoral Scan Models with Cephalometric Radiographs

2025-11-18

AI Summary

Clinical 3D–2D registration of intraoral scanner (IOS) models to lateral cephalometric radiographs suffers from convergence failure and anatomical misalignment due to projection magnification, geometric distortion, low crown contrast, and acquisition variability. To address this, we propose DentalSCR—a contour-guided framework. Its core innovation is a unified anatomical coordinate system—U-Midline Dental Axis—that enhances initialization robustness. We further introduce surface-driven digitally reconstructed radiograph (DRR) generation, coronal-axis perspective projection, and Gaussian splatting for accurate geometric modeling. A hierarchical coarse-to-fine symmetric bidirectional Chamfer distance metric optimizes the 2D similarity transformation. Evaluated on 34 clinical cases, DentalSCR significantly reduces landmark localization error, especially in posterior teeth, achieving sub-pixel contour alignment and clinically verifiable, high-fidelity registration.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements

Abstract

Reliable 3D-2D alignment between intraoral scan (IOS) models and lateral cephalometric radiographs is critical for orthodontic diagnosis, yet conventional intensity-driven registration methods struggle under real clinical conditions, where cephalograms exhibit projective magnification, geometric distortion, low-contrast dental crowns, and acquisition-dependent variation. These factors hinder the stability of appearance-based similarity metrics and often lead to convergence failures or anatomically implausible alignments. To address these limitations, we propose DentalSCR, a pose-stable, contour-guided framework for accurate and interpretable silhouette-to-contour registration. Our method first constructs a U-Midline Dental Axis (UMDA) to establish a unified cross-arch anatomical coordinate system, thereby stabilizing initialization and standardizing projection geometry across cases. Using this reference frame, we generate radiograph-like projections via a surface-based DRR formulation with coronal-axis perspective and Gaussian splatting, which preserves clinical source-object-detector magnification and emphasizes external silhouettes. Registration is then formulated as a 2D similarity transform optimized with a symmetric bidirectional Chamfer distance under a hierarchical coarse-to-fine schedule, enabling both large capture range and subpixel-level contour agreement. We evaluate DentalSCR on 34 expert-annotated clinical cases. Experimental results demonstrate substantial reductions in landmark error-particularly at posterior teeth-tighter dispersion on the lower jaw, and low Chamfer and controlled Hausdorff distances at the curve level. These findings indicate that DentalSCR robustly handles real-world cephalograms and delivers high-fidelity, clinically inspectable 3D--2D alignment, outperforming conventional baselines.

Problem

• Aligning 3D intraoral scans with 2D cephalometric radiographs for orthodontic diagnosis
• Overcoming limitations of conventional registration methods under clinical conditions
• Addressing projective magnification and low-contrast issues in cephalometric radiographs

Innovation

• UMDA establishes unified anatomical coordinate system
• Surface-based DRR generates radiograph-like silhouette projections
• Hierarchical Chamfer distance optimizes 2D similarity transform