Web: http://arxiv.org/abs/2206.08778

June 20, 2022, 1:13 a.m. | Weiwei Cui, Yaqi Wang, Qianni Zhang, Huiyu Zhou, Dan Song, Xingyong Zuo, Gangyong Jia, Liaoyuan Zeng

cs.CV updates on arXiv.org arxiv.org

3D tooth segmentation is a prerequisite for computer-aided dental diagnosis
and treatment. However, segmenting all tooth regions manually is subjective and
time-consuming. Recently, deep learning-based segmentation methods produce
convincing results and reduce manual annotation efforts, but it requires a
large quantity of ground truth for training. To our knowledge, there are few
tooth data available for the 3D segmentation study. In this paper, we establish
a fully annotated cone beam computed tomography dataset CTooth with tooth gold
standard. This dataset …

3d arxiv benchmark cv dataset images on segmentation

More from arxiv.org / cs.CV updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY