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

作者全名："Cui, Zhiming; Fang, Yu; Mei, Lanzhuju; Zhang, Bojun; Yu, Bo; Liu, Jiameng; Jiang, Caiwen; Sun, Yuhang; Ma, Lei; Huang, Jiawei; Liu, Yang; Zhao, Yue; Lian, Chunfeng; Ding, Zhongxiang; Zhu, Min; Shen, Dinggang"

作者地址："[Cui, Zhiming; Fang, Yu; Mei, Lanzhuju; Liu, Jiameng; Jiang, Caiwen; Sun, Yuhang; Ma, Lei; Huang, Jiawei; Shen, Dinggang] ShanghaiTech Univ, Sch Biomed Engn, Shanghai 201210, Peoples R China; [Cui, Zhiming] Univ Hong Kong, Dept Comp Sci, Hong Kong 999077, Peoples R China; [Cui, Zhiming; Shen, Dinggang] Shanghai United Imaging Intelligence Co Ltd, Shanghai 200030, Peoples R China; [Zhang, Bojun; Zhu, Min] Shanghai Jiao Tong Univ, Shanghai Peoples Hosp 9, Shanghai 200011, Peoples R China; [Yu, Bo] Hangzhou Med Coll, Sch Publ Hlth, Hangzhou 310013, Peoples R China; [Liu, Yang] Chongqing Med Univ, Dept Orthodont, Stomatol Hosp, Chongqing 401147, Peoples R China; [Zhao, Yue] Chongqing Univ Posts & Telecommun, Sch Commun & Informat Engn, Chongqing 400065, Peoples R China; [Lian, Chunfeng] Xi An Jiao Tong Univ, Sch Math & Stat, Xian 710049, Peoples R China; [Ding, Zhongxiang] Zhejiang Univ, Hangzhou Peoples Hosp 1, Dept Radiol, Hangzhou 310006, Peoples R China"

通信作者："Shen, DG (通讯作者)，ShanghaiTech Univ, Sch Biomed Engn, Shanghai 201210, Peoples R China.; Shen, DG (通讯作者)，Shanghai United Imaging Intelligence Co Ltd, Shanghai 200030, Peoples R China.; Zhu, M (通讯作者)，Shanghai Jiao Tong Univ, Shanghai Peoples Hosp 9, Shanghai 200011, Peoples R China.; Zhao, Y (通讯作者)，Chongqing Univ Posts & Telecommun, Sch Commun & Informat Engn, Chongqing 400065, Peoples R China.; Lian, CF (通讯作者)，Xi An Jiao Tong Univ, Sch Math & Stat, Xian 710049, Peoples R China.; Ding, ZX (通讯作者)，Zhejiang Univ, Hangzhou Peoples Hosp 1, Dept Radiol, Hangzhou 310006, Peoples R China."

来源：NATURE COMMUNICATIONS

ESI学科分类：　

WOS号：WOS:000784997300036

JCR分区：Q1

影响因子：16.6

年份：2022

卷号：13

期号：1

开始页：　

结束页：　

文献类型：Article

关键词：　

摘要："Accurate delineation of individual teeth and alveolar bones from dental cone-beam CT images is an essential step in digital dentistry for precision dental healthcare. Here, the authors present a deep learning system for efficient, precise, and fully automatic segmentation of real-patient CBCT images presenting highly variable appearances. 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."

基金机构："National Natural Science Foundation of China [62131015]; Science and Technology Commission of Shanghai Municipality (STCSM) [21010502600]; Key R&D Program of Guangdong Province, China [2021B0101420006]"

基金资助正文："This work was supported in part by National Natural Science Foundation of China (grant number 62131015), Science and Technology Commission of Shanghai Municipality (STCSM) (grant number 21010502600), and The Key R&D Program of Guangdong Province, China (grant number 2021B0101420006)."