从完整和部分口内扫描到牙冠提案：一种基于分类的修复辅助流程
From Full and Partial Intraoral Scans to Crown Proposal: A Classification-Guided Restoration Assistance Pipeline
AI总结：
本文提出一种基于分类的牙冠辅助流程，通过口内扫描和目标FDI牙齿编号输入，生成患者特异性的牙冠提案。流程包括数据准备、分割和牙冠生成三个阶段，采用分类-对齐策略提升部分扫描分割精度，最终生成高精度的牙冠模型。
摘要：
单单位牙冠修复是临床牙科中最常见的手术之一，CAD/CAM工作流程现在可以直接从口内扫描设计牙冠。部分扫描通常优于完整牙弓扫描，因为 stitching 错误更少，但大多数基于完整牙弓训练的分割网络在部分扫描上失败，而端到端生成的牙冠方法常产生过平滑的表面，丢失咬合细节。我们提出了一种端到端流程，输入原始口内扫描和目标FDI牙齿编号，输出初始的患者特异牙冠提案供临床医生完善。该流程有三个阶段：(I) 数据准备和姿态标准化；(II) 通过扫描类型路由的分割；(III) 通过上下文感知检索和Blender基于的拟合生成牙冠提案。我们通过分类-对齐策略解决部分扫描分割问题：DGCNN分类器将扫描分为五种解剖类型之一，然后粗到细的RANSAC+ICP配准标准化下颌坐标框架，随后通过图割优化细化牙齿-牙龈边界。在1958个部分扫描上训练，该流程在17个语义类别上达到宏平均DSC 0.9249，召回率0.8919，精确率0.9615；经过微调的完整牙弓模型达到DSC 0.9347。准备的牙齿及其近中和远中邻居达到DSC 0.9468-0.9569，具有亚毫米级的Centroid误差（0.2666-0.2774 mm）。这些质心锚定使用DGCNN嵌入和余弦相似度的检索模块，随后通过样条引导的对齐和Blender Python API细化。该流程在2.5-3.5分钟内生成初步的牙冠壳，提供了一种实用的替代方案，用于端到端生成方法。
英文摘要：
Single-unit crown restoration is among the most common procedures in clinical dentistry, with CAD/CAM workflows now designing crowns directly from intraoral scans. Partial scans are often preferred over full-arch scans for single-unit cases due to fewer stitching errors, yet most segmentation networks trained on full arches fail on partial scans, while end-to-end generative crown methods often produce over-smoothed surfaces that lose occlusal detail. We propose an end-to-end pipeline that takes a raw intraoral scan and target FDI tooth number as input and outputs an initial, patient-specific crown proposal for clinician refinement. The pipeline has three phases: (I) data preparation and pose standardization; (II) segmentation routed by scan type; and (III) crown proposal generation via context-aware retrieval and Blender-based fitting. We address partial-scan segmentation through a classify-then-align strategy: a DGCNN classifier categorizes the scan into one of five anatomical types, then coarse-to-fine RANSAC+ICP registration standardizes the jaw coordinate frame, followed by graph-cut optimization to refine tooth-gingival boundaries. Trained on 1,958 partial scans, the pipeline achieves macro-average DSC 0.9249, Recall 0.8919, and Precision 0.9615 across 17 semantic classes; a fine-tuned full-arch model reaches DSC 0.9347. The prepared tooth and its mesial and distal neighbors achieve DSC 0.9468-0.9569 with sub-millimeter Centroid Errors (0.2666-0.2774 mm). These centroids anchor a retrieval module using DGCNN embeddings and cosine similarity over neighboring and opposing teeth, followed by spline-guided alignment and Blender Python API refinement. The pipeline produces a preliminary crown shell in 2.5-3.5 minutes, offering a practical alternative to end-to-end generative approaches.
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。
