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

Author:

Cui ZhimingORCID,Fang YuORCID,Mei LanzhujuORCID,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

Abstract

AbstractAccurate 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.

Funder

National Natural Science Foundation of China

Science and Technology Commission of Shanghai Municipality

Publisher

Springer Science and Business Media LLC

Subject

General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry,Multidisciplinary

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