用少量标注数据和谱变形，从杂乱扫描中自动建模婴儿头骨形状。
SCALP: Semi-Supervised Statistical Shape Modeling from Imperfect 3D Photogrammetry via Landmark-Anchored Spectral Warp
- 用半监督点变换器定位关键点，减少人工标注
- 通过谱变形生成稠密对应关系，自动分离头颅与噪声
- 适合临床医生做无辐射头型分析，无需复杂预处理
基于对应关系的统计形状建模对群体形态分析至关重要，但传统流程依赖干净、完整注册的表面。真实临床摄影测量扫描常含噪声、不完整且杂乱，限制了无辐射表面成像作为婴儿颅缝早闭诊断中替代计算机断层扫描（CT）的安全方法。我们提出SCALP（半监督对应关系通过地标定位与谱变形），一个两阶段框架，可直接从原始不完美表面扫描构建一致的形状模型。首先，半监督点变换器利用少量专家标注数据与大量未标注数据，以极低标注成本准确定位颅面关键点。其次，这些关键点锚定拉普拉斯-贝尔特拉米谱变形，将解剖模板变形为密集对应关系，自然分离头颅与周边扫描杂波，无需手动预处理。在婴儿摄影测量扫描上的实验表明，SCALP始终优于最先进的无监督点云方法，为客观、无辐射的头型分析提供了临床可行路径。
原文摘要 · Abstract (English)
Correspondence-based statistical shape modeling (SSM) is vital for population-level morphometric analysis, but conventional pipelines assume clean, fully registered surfaces. Real-world clinical photogrammetry scans are often noisy, partial, and cluttered, hindering the adoption of radiation-free surface imaging as a safe alternative to computed tomography (CT) for infant craniosynostosis. We present SCALP (Semi-supervised Correspondence via lAndmark Localization and sPectral warping), a two-stage framework that constructs consistent shape models directly from raw, imperfect surface scans. First, a semi-supervised Point Transformer leverages a small expert-annotated dataset alongside a large unlabeled cohort to accurately localize craniofacial landmarks with minimal annotation overhead. Second, these landmarks anchor a Laplace--Beltrami spectral deformation of an anatomical template, generating dense correspondences while naturally isolating the cranium from peripheral scanning clutter without manual preprocessing. Experiments on infant photogrammetry scans demonstrate that SCALP consistently outperforms state-of-the-art unsupervised point-cloud approaches, offering a clinically practical pathway toward objective, radiation-free head shape analysis.
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