从皮肤表面预测隐藏骨骼点，误差仅2.97毫米
Surface-to-Skeleton 3D Cephalometry: Estimating Hidden Skeletal Landmarks from CT-Derived External Soft-Tissue Surfaces
- 用同一扫描数据构建表面到骨骼映射模型，分离干扰因素
- 对40名患者测试，骨骼点平均误差2.97毫米，深部点也达3.03毫米
- 适合医学影像、正颌外科及个性化建模领域研究者参考
现有3D面部特征点方法仅定位可见皮肤点，但能否从外部软组织几何形状推断内部骨骼点尚不明确。本研究基于两所医院的240例临床CT扫描，构建了配对的外源性软组织点云与21个骨骼标志点及3个可见软组织标志点的锁定回顾协议。采用集成分层点云模型，在40例留出患者中实现骨骼点平均径向误差2.97毫米，深部或表面不可见点误差为3.03毫米。患者差异控制实验表明，个体特异性信号显著超越固定群体配置或全局相似性；覆盖范围消融分析显示依赖非前部几何结构。光学转移诊断揭示存在显著覆盖率相关与全局构型成分，尽管可部署的光学推断仍待解决。结果正面回答了受控可行性问题，并为隐藏骨骼点推断提供了基础。
原文摘要 · Abstract (English)
Existing 3D facial-landmark methods localize points on visible skin, but whether CT-defined internal skeletal landmarks can be inferred from external soft-tissue geometry remains unclear. We formulate a coordinate-consistent surface-to-skeleton task using same-acquisition CT-derived surfaces, separating estimation from optical-to-CT registration, scanner-domain, and acquisition-state effects, with coverage analyzed separately. From 240 clinical CT scans from two hospitals, we construct a locked retrospective protocol pairing CT-derived external soft-tissue point clouds with 21 skeletal landmarks and three visible soft-tissue landmarks. An integrated hierarchical point-cloud model achieves 2.97 mm mean radial error on skeletal landmarks and 3.03 mm on deep or surface-invisible landmarks in 40 held-out patients. Patient-mismatch controls support patient-specific signal beyond a fixed population configuration or global similarity alone, while coverage ablations indicate dependence on non-anterior geometry. Optical-transfer diagnostics reveal substantial coverage-related and global-configuration components, although deployable optical inference remains unresolved. These results answer the controlled feasibility question affirmatively and provide a basis for hidden skeletal landmark inference.
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