用AI自动定位头影测量点，实现3D影像的智能正畸评估
Automatic Cephalometric Landmark Localization on CBCT-Derived Digitally Reconstructed Radiographs for Skeletal Malocclusion Classification
- 基于视觉变换器的CephViT模型，自动识别头影测量关键点
- 在DRR图像上定位误差仅1.28毫米，准确率达92%
- 结果与人工标注相当，适合临床正畸智能诊断应用
人工头影测量点标注对颅面评估至关重要，但耗时且难规模化。本文提出CephViT，一种基于视觉变换器的2D侧位头影测量点自动定位模型，并评估其在骨骼错颌分类中的下游应用。CephViT在公开侧位片数据集上训练并测试，平均径向误差为1.28 ± 1.42毫米，3.0毫米容差下检测成功率达92.0%。由于评估队列来自3D CBCT扫描，每例生成对应的数字化重建放射影像（DRR）作为2D输入。通过统一坐标系归一化点位，使用共享地标进行骨骼错颌分类。基于DRR定位点的分类准确率为68.3%，与人工标注参考结果（70.0%）相当。结果表明，基于CBCT生成的DRR可实现自动化头影测量分析，支持骨骼错颌评估的可行性。
原文摘要 · Abstract (English)
Manual cephalometric landmark annotation is important for craniofacial assessment but is labor-intensive and difficult to scale. We introduce CephViT, a Vision Transformer-based model for automated 2D lateral cephalometric landmark localization, and evaluate its use in downstream skeletal malocclusion classification. CephViT was trained and benchmarked on a public lateral cephalogram dataset, achieving a mean radial error of 1.28 +/- 1.42 mm and a successful detection rate of 92.0% at 3.0 mm. Because the private evaluation cohort consisted of 3D CBCT scans, lateral cephalogram-like digitally reconstructed radiographs (DRRs) were generated from each volume and used as 2D inputs to the landmark localization model. Landmark coordinates were normalized into a common coordinate frame, and skeletal malocclusion classification was performed using landmarks shared between the reference and DRR-based pipelines. Classification performance using DRR-localized landmarks was comparable to that obtained using manually annotated reference landmarks, with accuracies of 70.0% and 68.3%, respectively. These results support the feasibility of automated cephalometric analysis on CBCT-derived DRRs for skeletal malocclusion assessment.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。