arXiv 2511.143432025-11-18cs.CV

轮廓到边缘配准：口腔内扫描模型与头影测量X光片的对齐

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

School of AI and Advanced Computing, Xi'an Jiaotong-Liverpool University, China（人工智能与先进计算学院，西安交通大学利物浦大学，中国）；School of Electrical Engineering, Electronics and Computer Science, University of Liverpool, United Kingdom（电子工程、电子科学与计算机科学学院，利物浦大学，英国）；School of Advanced Technology, Xi'an Jiaotong-Liverpool University, China（先进技术学院，西安交通大学利物浦大学，中国）；School of Physical Sciences, University of Liverpool, Liverpool, United Kingdom（物理科学学院，利物浦大学，英国）；School of Mathematics and Physics, Xi'an Jiaotong-Liverpool University, China（数学与物理学院，西安交通大学利物浦大学，中国）；Institute of Artificial Intelligence Innovation, Zhejiang University of Technology, China（人工智能创新研究院，浙江工业大学，中国）

Yiyi Miao, Taoyu Wu, Ji Jiang, Tong Chen, Zhe Tang, Zhengyong Jiang, Angelos Stefanidis, Limin Yu, Jionglong Su

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

针对口腔内扫描模型与头影测量X光片3D-2D对齐的临床挑战，提出DentalSCR轮廓引导框架，通过UMDA坐标系、类X光投影及分层优化，在34例病例中实现更优的地标精度与轮廓一致性，优于传统方法。

摘要：

口腔内扫描（IOS）模型与侧位头影测量X光片之间可靠的3D-2D对齐对正畸诊断至关重要，但传统的强度驱动配准方法在实际临床条件下表现不佳，头影图存在投影放大、几何畸变、低对比度牙冠和采集相关变异等问题。这些因素阻碍了基于外观的相似性度量的稳定性，常导致收敛失败或解剖学上不合理的对齐。为解决这些局限，我们提出DentalSCR，一种姿态稳定、轮廓引导的框架，用于准确且可解释的轮廓到边缘配准。该方法首先构建U型中线牙轴（UMDA）以建立统一的跨牙弓解剖坐标系，从而稳定初始化并标准化不同病例的投影几何。利用该参考框架，通过基于表面的DRR公式（带冠状轴透视和高斯喷涂）生成类X光片投影，保留临床源-物-探测器放大率并强调外部轮廓。配准随后被表述为2D相似变换，在分层从粗到细的调度下用对称双向Chamfer距离优化，实现大捕获范围和亚像素级边缘一致性。我们在34个专家注释的临床病例上评估DentalSCR，实验结果显示地标误差大幅减少（尤其是后牙处）、下颌分散度更紧密，曲线层面的Chamfer距离和受控Hausdorff距离较低。这些发现表明DentalSCR能稳健处理真实世界头影图，提供高保真、临床可检查的3D-2D对齐，优于传统基线。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。