ViSTooth: A Visualization Framework for Tooth Segmentation on Panoramic Radiograph
<|reference_start|>ViSTooth: A Visualization Framework for Tooth Segmentation on Panoramic Radiograph: 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.<|reference_end|>
@article{zhu2024vistooth:,
    title={ViSTooth: A Visualization Framework for Tooth Segmentation on Panoramic
  Radiograph},
    author={Shenji Zhu and Miaoxin Hu and Tianya Pan and Yue Hong and Bin Li and
  Zhiguang Zhou and Ting Xu},
    journal={arXiv preprint arXiv:2405.08573},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.08573},
    primaryClass={cs.HC}
}