  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.
