首页|Fully automatic AI segmentation of oral surgery-related tissues based on cone beam computed tomography images

Fully automatic AI segmentation of oral surgery-related tissues based on cone beam computed tomography images

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
Accurate segmentation of oral surgery-related tissues from cone beam computed tomography(CBCT)images can significantly accelerate treatment planning and improve surgical accuracy.In this paper,we propose a fully automated tissue segmentation system for dental implant surgery.Specifically,we propose an image preprocessing method based on data distribution histograms,which can adaptively process CBCT images with different parameters.Based on this,we use the bone segmentation network to obtain the segmentation results of alveolar bone,teeth,and maxillary sinus.We use the tooth and mandibular regions as the ROI regions of tooth segmentation and mandibular nerve tube segmentation to achieve the corresponding tasks.The tooth segmentation results can obtain the order information of the dentition.The corresponding experimental results show that our method can achieve higher segmentation accuracy and efficiency compared to existing methods.Its average Dice scores on the tooth,alveolar bone,maxillary sinus,and mandibular canal segmentation tasks were 96.5%,95.4%,93.6%,and 94.8%,respectively.These results demonstrate that it can accelerate the development of digital dentistry.

Yu Liu、Rui Xie、Lifeng Wang、Hongpeng Liu、Chen Liu、Yimin Zhao、Shizhu Bai、Wenyong Liu

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Beijing Yakebot Technology Co.,Ltd.,Beijing,China

School of Mechanical Engineering and Automation,Beihang University,Beijing,China

State Key Laboratory of Oral&Maxillofacial Reconstruction and Regeneration,National Clinical Research Center for Oral Diseases,Shaanxi Key Laboratory of Stomatology,Digital Center,School of Stomatology,The Fourth Military Medical University,Xi'an,China

Key Laboratory of Biomechanics and Mechanobiology of the Ministry of Education,Beijing Advanced Innovation Center for Biomedical Engineering,School of Biological Science and Medical Engineering,Beihang University,Beijing,China

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National Natural Science Foundation of China

81970987

2024

10.1038/s41368-024-00294-z

国际口腔科学杂志(英文版)
四川大学

国际口腔科学杂志(英文版)

CSTPCD
影响因子：0.684
ISSN：1674-2818
年,卷(期)：2024.16(3)