SCALP：基于地标锚定谱变形的不完美3D摄影测量半监督统计形状建模

中文摘要 AI 辅助

基于对应关系的统计形状建模（SSM）对群体水平的形态计量分析至关重要，但传统流程假设表面干净且已完全配准。现实中的临床摄影测量扫描往往存在噪声、不完整和杂乱的问题，阻碍了无辐射表面成像作为计算机断层扫描（CT）的安全替代方案用于婴儿颅缝早闭的应用。我们提出SCALP（半监督对应方法，通过地标定位和谱变形实现），这是一个两阶段框架，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Point Transformer利用少量专家标注数据集和大量未标注队列，以最小的标注开销准确定位颅面地标。其次，这些地标锚定解剖模板的拉普拉斯-贝尔特拉米谱变形，生成密集对应关系，同时自然将颅骨与周围扫描杂波分离，无需手动预处理。对婴儿摄影测量扫描的实验表明，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的途径。

英文摘要

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.

发表机构

• Scientific Computing and Imaging Institute, University of Utah（犹他大学科学计算与成像研究所）
• Kahlert School of Computing, University of Utah（犹他大学卡勒特计算机学院）
• Division of Pediatric Plastic Surgery, UPMC Children’s Hospital of Pittsburgh（匹兹堡大学医学中心儿童医院小儿整形外科）

机构由 AI 辅助整理，请以论文原文为准。