Automatic mandible segmentation from CT image using 3D fully convolutional neural network based on DenseASPP and attention gates.

Journal: International journal of computer assisted radiology and surgery
Published Date: Jul 21, 2021

Abstract

PURPOSE: In cranio-maxillofacial surgery, it is of great clinical significance to segment mandible accurately and automatically from CT images. However, the connected region and blurred boundary in teeth and condyles make the process challenging. At present, the mandible is commonly segmented by experienced doctors using manually or semi-automatic methods, which is time-consuming and has poor segmentation consistency. In addition, existing automatic segmentation methods still have problems such as region misjudgment, low accuracy, and time-consuming.

Authors

  • 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.
  • Jiannan Liu
    Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Dingzhong Zhang
    Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Room 805, Dongchuan Road 800, Minhang District, Shanghai, 200240, China.
  • Zijie Zhou
    Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Xiaoyi Jiang
    Faculty of Mathematics and Computer Science, University of Münster, Münster, Germany.
  • Chenping Zhang
    Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 639, ZhiZao JuRd, Shanghai, 200011, China. zhang.chenping@hotmail.com.
  • Xiaojun Chen
    Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China.

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