Journal of Shanghai Jiao Tong University ›› 2022, Vol. 56 ›› Issue (11): 1509-1517.doi: 10.16183/j.cnki.jsjtu.2021.103

• Biomedical Engineering • Previous Articles     Next Articles

Design of Mandibular Angle Osteotomy Plane Based on Point Cloud Semantic Segmentation Algorithm

LÜ Chaofan1, YAN Yingjie2, LIN Li2,3, CHAI Gang2, BAO Jinsong1()   

  1. 1. College of Mechanical Engineering, Donghua University, Shanghai 201620, China
    2. Department of Plastic and Reconstructive Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
    3. Institute of Forming Technology and Equipment, Shanghai Jiao Tong University, Shanghai 200030, China
  • Received:2021-04-05 Online:2022-11-28 Published:2022-12-02
  • Contact: BAO Jinsong


Mandibular angle osteotomy is a popular craniofacial plastic surgery in recent years. Usually, preoperative planning of mandibular angle osteotomy is completed by an experienced doctor, which is cumbersome and time-consuming. In order to improve the efficiency of osteotomy planning, a design method of mandibular angle osteotomy plane based on point cloud semantic segmentation network is proposed. After three-dimensional reconstruction of the skull computer tomography (CT) scan data, the three-dimensional model of the mandible is converted into point cloud data through uniform sampling. The resection area of the mandible is predicted by the proposed algorithm, which is used to calculate the mandibular angle osteotomy plane. The proposed semantic segmentation network mainly includes 2 parts: a local feature extraction layer based on the attention mechanism, which is used to extract fine-grained local structure information, and a non-local feature extraction layer based on Transformer, which is used to extract the global context information of the point cloud. On the constructed mandible semantic segmentation data set, the proposed algorithm is compared with other point cloud semantic segmentation algorithms. The results show that the proposed algorithm can achieve the best prediction of the mandibular angle resection area, which is better than current common point cloud semantic segmentation algorithms.

Key words: mandibular angle osteotomy, point cloud segmentation, self-attention, deep learning

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