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.

翻译：牙齿分割是计算机辅助诊断牙科疾病的关键步骤。众多机器学习模型已被应用于牙齿全景X线片的牙齿分割任务中。然而，由于牙齿形态复杂、类别多样以及机器学习样本集不完整，实现精确的牙齿分割仍是一项困难的任务。本文提出ViSTooth——一种面向全景X线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，并提出一组领域度量指标（包括牙形、牙位和牙角）来评估分割牙齿的准确性。随后，我们将牙齿表示为高维向量，并在低维空间中可视化其分布，使专家能够轻松观察具有特定度量指标的牙齿。进一步地，我们利用专家标注的牙齿扩充样本集，并迭代训练牙齿分割模型。最后，通过案例研究和专家研究验证了ViSTooth在辅助专家利用专业知识实现精确牙齿分割方面的有效性和可用性。