Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans-Reference-Cited by-同舟云学术

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

Published:2022-11 Issue:11 Volume:41 Page:3158-3166
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Wu Tai-Hsien¹^ORCID,Lian Chunfeng²,Lee Sanghee¹,Pastewait Matthew³,Piers Christian⁴^ORCID,Liu Jie¹,Wang Fan⁵,Wang Li⁶^ORCID,Chiu Chiung-Ying⁷,Wang Wenchi⁷,Jackson Christina⁷,Chao Wei-Lun⁸,Shen Dinggang⁹^ORCID,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

Link

https://ieeexplore.ieee.org/ielam/42/9931396/9789163-aam.pdf

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