ViSTooth: A Visualization Framework for Tooth Segmentation on Panoramic Radiograph

TL;DR

ViSTooth is a visualization framework that enhances tooth segmentation accuracy on panoramic radiographs by integrating domain metrics, high-dimensional representation, and expert-guided iterative training, improving clinical diagnosis support.

Contribution

The paper introduces a novel visualization framework combining Mask R-CNN, domain metrics, and high-dimensional visualization for improved tooth segmentation accuracy.

Findings

Effective visualization of tooth distribution in low-dimensional space.

Improved segmentation accuracy through iterative expert-guided sample expansion.

Demonstrated usability and effectiveness via case and expert studies.

Abstract

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.