表面到骨骼的3D头影测量：从CT衍生的外部软组织表面估计隐藏的骨骼标志点

中文摘要 AI 辅助

现有的3D面部标志点方法可定位可见皮肤上的点，但能否从外部软组织几何结构推断CT定义的内部骨骼标志点尚不明确。我们采用同一采集的CT衍生表面，构建了坐标一致的表面到骨骼任务，将估计与光学到CT配准、扫描仪域及采集状态效应分离，并单独分析覆盖度。基于两家医院的240份临床CT扫描，我们构建了锁定的回顾性方案，配对CT衍生的外部软组织点云与21个骨骼标志点及3个可见软组织标志点。集成层次化点云模型在40个留存患者的骨骼标志点上实现2.97mm的平均径向误差，在深部或不可见表面标志点上实现3.03mm的平均径向误差。患者不匹配对照实验证实，模型能捕捉超出固定群体配置或仅全局相似性的患者特异性信号，而覆盖度消融实验表明其依赖于非前部几何结构。光学转移诊断显示存在大量与覆盖度相关及全局配置的成分，但可部署的光学推理仍未解决。这些结果肯定地回答了受控可行性问题，并为隐藏骨骼标志点的推断提供了基础。

英文摘要

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.

发表机构

• Keio University（庆应义塾大学）
• Tokyo Dental College（东京齿科大学）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

备注

Accepted to AI4M3D Workshop at ECCV 2026