用拓扑分析方法自动识别牙科影像中的牙齿类型和病变，准确率超96%。
Topology of a Smile: Persistent Homology in Dental Imaging
- 通过持久同调分析牙科影像的形状特征，捕捉多尺度结构信息
- 牙齿分类准确率达97.67%，诊断准确率达96.77%，显著优于传统CNN
- 适合需要高精度自动化分析的牙科影像研究与临床辅助系统
CBCT（锥形束计算机断层扫描）提供高分辨率三维图像，广泛用于牙科诊断与治疗规划。然而，人工分析耗时费力，促使研究者探索自动化方法以实现解剖结构的分类与分割，从而识别牙齿类型及病灶。本文提出一种基于持久同调（persistent homology）的拓扑数据分析方法，结合支持向量机，实现对CBCT影像中牙齿的分类与病理诊断。该方法在多个尺度上识别数据的连通分量、孔洞与空腔等几何特征，有效捕捉牙齿形态的拓扑特性。实验结果表明，该方法在牙齿标注任务上的平均准确率达到97.67%，在诊断任务上达到96.77%，显著优于在同一数据集上训练的CNN模型（分别为70.27%和86.67%），展现了在牙科影像自动化分析中的先进性与潜力。
原文摘要 · Abstract (English)
CBCT (Cone Beam Computed Tomography) scans provide detailed three-dimensional images, widely used in dentistry for diagnostic and treatment planning tasks. While invaluable, analyzing and documenting these scans is labor-intensive, prompting efforts to automate key steps like the classification and segmentation of anatomical structures to identify tooth types and associated pathologies. In this article, we propose an approach to automation that leverages persistent homology, a framework from topological data analysis that studies the shape of data by identifying features like connected components, holes, and voids across multiple scales. Persistent homology, together with a support vector machine, allows us to classify teeth in a CBCT scan and to perform diagnostics. Our method advances the state of the art, reaching average accuracy scores of 97.67% for tooth-labeling and 96.77% for diagnostic tasks, outperforming a CNN trained on the same data with accuracy of 70.27% and 86.67%, respectively.
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