{"title":"Fully automatic AI segmentation of oral surgery-related tissues based on cone beam computed tomography images","authors":"Yu Liu, Rui Xie, Lifeng Wang, Hongpeng Liu, Chen Liu, Yimin Zhao, Shizhu Bai, Wenyong Liu","doi":"10.1038/s41368-024-00294-z","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":14191,"journal":{"name":"International Journal of Oral Science","volume":null,"pages":null},"PeriodicalIF":10.8000,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Oral Science","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1038/s41368-024-00294-z","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"DENTISTRY, ORAL SURGERY & MEDICINE","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
The International Journal of Oral Science covers various aspects of oral science and interdisciplinary fields, encompassing basic, applied, and clinical research. Topics include, but are not limited to:
Oral microbiology
Oral and maxillofacial oncology
Cariology
Oral inflammation and infection
Dental stem cells and regenerative medicine
Craniofacial surgery
Dental material
Oral biomechanics
Oral, dental, and maxillofacial genetic and developmental diseases
Craniofacial bone research
Craniofacial-related biomaterials
Temporomandibular joint disorder and osteoarthritis
The journal publishes peer-reviewed Articles presenting new research results and Review Articles offering concise summaries of specific areas in oral science.