Deep learning-based automatic segmentation of bone graft material after maxillary sinus augmentation.

Journal: Clinical oral implants research
PMID:

Abstract

OBJECTIVES: To investigate the accuracy and reliability of deep learning in automatic graft material segmentation after maxillary sinus augmentation (SA) from cone-beam computed tomography (CBCT) images.

Authors

  • Baoxin Tao
    Department of 2nd Dental Center, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine; National Clinical Research Center for Oral Diseases; Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, China.
  • Jiangchang Xu
    Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University , Shanghai , China.
  • Jie Gao
    Department of Nephrology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
  • Shamin He
    Department of Second Dental Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Shuanglin Jiang
    Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China.
  • Feng Wang
    Department of Oncology, Binzhou Medical University Hospital, Binzhou, Shandong, China.
  • Xiaojun Chen
    Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China.
  • Yiqun Wu
    Department of Second Dental Clinic and Oral Implantology, Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University, School of Medicine, National Clinical Research Center for Oral Disease, Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, 639, Zhizaoju Road, Shanghai 200011, China.