发表机构
Scientific Computing and Imaging Institute, University of Utah; Kahlert School of Computing, University of Utah; Division of Pediatric Plastic Surgery, UPMC Children’s Hospital of Pittsburgh（犹他大学科学计算与成像研究所; 犹他大学卡勒特计算学院; 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。AI 中文总结
研究针对临床三维头部摄影的伪影问题，提出TRACE框架，可直接从受污染数据构建抗伪影SSMs，在多骨干网络上均提升了形状模型质量，为颅缝早闭分析提供了可扩展基础。
AI 中文摘要
颅缝早闭严重程度分析越来越依赖统计形状模型（SSMs）来量化颅骨形态，但大多数现有工作流程依赖计算机断层扫描或经过大量整理的三维（3D）摄影。原始临床三维摄影提供了无辐射且可重复的替代方案，但通常包含肩部、手部、头发、衣物、扫描仪噪声及不完整边界，这些会破坏对应关系。我们提出了模板约束型抗伪影感知对应估计（TRACE）框架，这是一种直接从受伪影污染的临床三维头部摄影构建SSMs的无监督方法。TRACE从原始点云中预测稀疏的解剖学对应头部表面控制点，通过由粗到细的表面感知变形级联对其进行优化，并使用薄板样条变形将干净模板网格变形为受试者特异性头部重建。这种模板约束公式在临床相关头部解剖结构上保持密集对应关系，同时抑制非头部伪影。对应模块与点云编码器解耦，使相同的变形流水线可与不同骨干网络配对，包括PointNet、DGCNN和Point Transformer V3。在所有骨干网络上，TRACE相比现有SSM方法显著改善了表面采样、拓扑保留和形状模型质量，为基于摄影的颅缝早闭形状分析提供了可扩展基础，且当有合适的干净模板时，该框架可扩展至其他受伪影污染的表面扫描。
英文摘要
Craniosynostosis severity analysis increasingly relies on statistical shape models (SSMs) to quantify cranial morphology, but most existing workflows depend on computed tomography or heavily curated three-dimensional (3D) photographs. Raw clinical 3D photographs provide a radiation-free and repeatable alternative, yet often contain shoulders, hands, hair, clothing, scanner noise, and incomplete boundaries that corrupt correspondences. We introduce the Template-constrained Robust Artifact-aware Correspondence Estimation (TRACE) framework, an unsupervised method for constructing SSMs directly from artifact-contaminated clinical 3D head photographs. TRACE predicts sparse anatomically corresponding head-surface control points from the raw point cloud, refines them through a coarse-to-fine Surface-Aware Deformation cascade, and uses thin-plate spline warping to deform a clean template mesh into a subject-specific head reconstruction. This template-constrained formulation keeps dense correspondences on clinically relevant head anatomy while suppressing non-head artifacts. The correspondence module is decoupled from the point-cloud encoder, enabling the same deformation pipeline to be paired with different backbones, including PointNet, DGCNN, and Point Transformer V3. Across all backbones, TRACE substantially improves surface sampling, topology preservation, and shape-model quality over prior SSM methods, providing a scalable foundation for photograph-based craniosynostosis shape analysis and a framework that may extend to other artifact-contaminated surface scans when an appropriate clean template is available.
CommentsAccepted at ShapeMI workshop at MICCAI 2026
