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

doi:10.1038/s41467-022-29637-2

	A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images
	Cui, Zhiming1,2,3 ; Fang, Yu1 ; Mei, Lanzhuju1 ; Zhang, Bojun 4; Yu, Bo 5; Liu, Jiameng1 ; Jiang, Caiwen1 ; Sun, Yuhang1 ; Ma, Lei1 ; Huang, Jiawei1 ; Liu, Yang 6; Zhao, Yue 7; Lian, Chunfeng 8; Ding, Zhongxiang 9; Zhu, Min 4; Shen, Dinggang1,3
	2022-12-01
发表期刊	NATURE COMMUNICATIONS (IF:14.7[JCR-2023],16.1[5-Year])
EISSN	2041-1723
卷号	13 期号:1
发表状态	已发表
DOI	10.1038/s41467-022-29637-2
摘要	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.
URL	查看原文
收录类别	SCIE
语种	英语
Scopus 记录号	2-s2.0-85128405129
来源库	Scopus
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/176083
专题	生物医学工程学院物质科学与技术学院_特聘教授组_孙予罕组信息科学与技术学院_硕士生信息科学与技术学院_博士生生物医学工程学院_PI研究组_沈定刚组生物医学工程学院_PI研究组_崔智铭组
作者单位	1.School of Biomedical Engineering,ShanghaiTech University,Shanghai,201210,China 2.Department of Computer Science,The University of Hong Kong,999077,Hong Kong 3.Shanghai United Imaging Intelligence Co.,Ltd.,Shanghai,200030,China 4.Shanghai Ninth People’s Hospital,Shanghai Jiao Tong University,Shanghai,200011,China 5.School of Public Health,Hangzhou Medical College,Hangzhou,310013,China 6.Department of Orthodontics,Stomatological Hospital of Chongqing Medical University,Chongqing,401147,China 7.School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing,400065,China 8.School of Mathematics and Statistics,Xi’an Jiaotong University,Xi’an,710049,China 9.Department of Radiology,Hangzhou First People’s Hospital,Zhejiang University,Hangzhou,310006,China
第一作者单位	生物医学工程学院
第一作者的第一单位	生物医学工程学院
推荐引用方式 GB/T 7714	Cui, Zhiming,Fang, Yu,Mei, Lanzhuju,et al. A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images[J]. NATURE COMMUNICATIONS,2022,13(1).
APA	Cui, Zhiming.,Fang, Yu.,Mei, Lanzhuju.,Zhang, Bojun.,Yu, Bo.,...&Shen, Dinggang.(2022).A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images.NATURE COMMUNICATIONS,13(1).
MLA	Cui, Zhiming,et al."A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images".NATURE COMMUNICATIONS 13.1(2022).