Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans

Author:

Wu Tai-Hsien1ORCID,Lian Chunfeng2,Lee Sanghee1,Pastewait Matthew3,Piers Christian4ORCID,Liu Jie1,Wang Fan5,Wang Li6ORCID,Chiu Chiung-Ying7,Wang Wenchi7,Jackson Christina7,Chao Wei-Lun8,Shen Dinggang9ORCID,Ko Ching-Chang1

Affiliation:

1. Division of Orthodontics, College of Dentistry, The Ohio State University, Columbus, OH, USA

2. School of Mathematics and Statistics, Xi’an Jiaotong University, Shaanxi, Xi’an, China

3. United States Air Force, Kadena, Japan

4. Small and Piers Orthodontics, Morganton, NC, USA

5. Department of Biomedical Engineering, Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, Shaanxi, Xi’an, China

6. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

7. SOVE Inc, Columbus, OH, USA

8. Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA

9. School of Biomedical Engineering, ShanghaiTech University, Shanghai, China

Funder

The Ohio State University College of Dentistry through the National Institutes of Health (NIH)/National Institute of Dental and Craniofacial Research

NSF

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

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