微笑的拓扑：牙科成像中的持续同调
Topology of a Smile: Persistent Homology in Dental Imaging
- ETH Zürich（苏黎世联邦理工学院）
- PSU（宾夕法尼亚州立大学）
- University of Zürich（苏黎世大学）
- University of Fribourg（弗里堡大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究将拓扑数据分析中的持续同调与支持向量机结合，实现CBCT扫描中牙齿分类与诊断，准确率优于CNN，为牙科成像的自动化分析提供了新方法。
AI中文摘要：
锥形束计算机断层扫描（CBCT）扫描可提供详细的三维图像，广泛应用于牙科的诊断和治疗规划任务。尽管具有重要价值，但分析和记录这些扫描结果的工作十分繁琐，因此研究人员致力于自动化关键步骤，如解剖结构的分类和分割，以识别牙齿类型及相关病理状况。本文提出一种利用持续同调（persistent homology，拓扑数据分析领域的框架，通过识别多尺度下的连通分量、孔洞和空隙等特征来研究数据形状）的自动化方法。该方法结合支持向量机，可对CBCT扫描中的牙齿进行分类并完成诊断任务。本方法提升了现有技术水平，牙齿标注的平均准确率达97.67%，诊断任务的平均准确率达96.77%，分别优于在相同数据上训练的卷积神经网络（CNN）的70.27%和86.67%的准确率。
英文摘要：
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.
