ViSTooth：一种用于全景X光片牙齿分割的可视化框架
ViSTooth: A Visualization Framework for Tooth Segmentation on Panoramic Radiograph
AI总结：
针对全景X光片牙齿分割因样本不全、形态复杂导致精度不足的问题，提出可视化框架ViSTooth，结合Mask R-CNN初分割、领域指标评估、低维可视化交互与专家引导的样本迭代扩充，提升分割准确率与可用性。
摘要：
牙齿分割是牙科疾病计算机辅助诊断的关键步骤。目前已有大量机器学习模型被用于牙科全景X光片的牙齿分割任务，但由于牙齿形状复杂、类别多样，且机器学习可用的样本集不完整，实现精准牙齿分割仍是一项难题。本文提出ViSTooth，一种面向牙科全景X光片牙齿分割的可视化框架。首先，我们采用Mask R-CNN进行初步牙齿分割，并提出一组领域指标来评估分割牙齿的准确率，指标涵盖牙齿形状、牙齿位置和牙齿角度。随后，我们用高维向量表示牙齿，并在低维空间中可视化其分布，专家可在其中轻松观察具备特定指标特征的牙齿。进一步地，我们利用专家指定的牙齿扩充样本集，迭代训练牙齿分割模型。最后，我们通过案例研究和专家研究验证了ViSTooth的有效性与可用性，该框架可在专家知识引导下辅助专家实现精准的牙齿分割。
英文摘要：
Tooth segmentation is a key step for computer aided diagnosis of dental diseases. Numerous machine learning models have been employed for tooth segmentation on dental panoramic radiograph. However, it is a difficult task to achieve accurate tooth segmentation due to complex tooth shapes, diverse tooth categories and incomplete sample set for machine learning. In this paper, we propose ViSTooth, a visualization framework for tooth segmentation on dental panoramic radiograph. First, we employ Mask R-CNN to conduct preliminary tooth segmentation, and a set of domain metrics are proposed to estimate the accuracy of the segmented teeth, including tooth shape, tooth position and tooth angle. Then, we represent the teeth with high-dimensional vectors and visualize their distribution in a low-dimensional space, in which experts can easily observe those teeth with specific metrics. Further, we expand the sample set with the expert-specified teeth and train the tooth segmentation model iteratively. Finally, we conduct case study and expert study to demonstrate the effectiveness and usability of our ViSTooth, in aiding experts to implement accurate tooth segmentation guided by expert knowledge.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
