A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images

Zhiming Cui, Yu Fang, Lanzhuju Mei, Bojun Zhang, Bo Yu, Jiameng Liu, Caiwen Jiang, Yuhang Sun, Lei Ma, Jiawei Huang, Yang Liu, Yue Zhao (), Chunfeng Lian (), Zhongxiang Ding (), Min Zhu () and Dinggang Shen ()
Additional contact information
Zhiming Cui: ShanghaiTech University
Yu Fang: ShanghaiTech University
Lanzhuju Mei: ShanghaiTech University
Bojun Zhang: Shanghai Jiao Tong University
Bo Yu: Hangzhou Medical College
Jiameng Liu: ShanghaiTech University
Caiwen Jiang: ShanghaiTech University
Yuhang Sun: ShanghaiTech University
Lei Ma: ShanghaiTech University
Jiawei Huang: ShanghaiTech University
Yang Liu: Stomatological Hospital of Chongqing Medical University
Yue Zhao: Chongqing University of Posts and Telecommunications
Chunfeng Lian: Xi’an Jiaotong University
Zhongxiang Ding: Hangzhou First People’s Hospital, Zhejiang University
Min Zhu: Shanghai Jiao Tong University
Dinggang Shen: ShanghaiTech University

Nature Communications, 2022, vol. 13, issue 1, 1-11

Abstract: Abstract Accurate delineation of individual teeth and alveolar bones from dental cone-beam CT (CBCT) images is an essential step in digital dentistry for precision dental healthcare. In this paper, we present an AI system for efficient, precise, and fully automatic segmentation of real-patient CBCT images. Our AI system is evaluated on the largest dataset so far, i.e., using a dataset of 4,215 patients (with 4,938 CBCT scans) from 15 different centers. This fully automatic AI system achieves a segmentation accuracy comparable to experienced radiologists (e.g., 0.5% improvement in terms of average Dice similarity coefficient), while significant improvement in efficiency (i.e., 500 times faster). In addition, it consistently obtains accurate results on the challenging cases with variable dental abnormalities, with the average Dice scores of 91.5% and 93.0% for tooth and alveolar bone segmentation. These results demonstrate its potential as a powerful system to boost clinical workflows of digital dentistry.

Date: 2022
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DOI: 10.1038/s41467-022-29637-2

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