Automatic Image Annotation and Deep Learning for Tooth CT Image Segmentation

  • Miao Gou
  • Yunbo RaoEmail author
  • Minglu Zhang
  • Jianxun Sun
  • Keyang Cheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11902)


Recently, convolutional networks show great ability dealing with the problem of biomedical imaging, such as tooth image segmentation. In this paper, we propose a novel tooth-based computer tomography (CT) image segmentation approach that integrates U-Net with a level set model. Compared with a single U-Net, our method uses the level set method to build the mask for CT images. This allows automatic annotation in our model, improving the efficiency on image segmentation. Furthermore, we make some changes to the origin U-Net structure for the feasibility to images of any sizes. Using the combination of these two models, our integrated method shows its superiority dealing with problems on tooth image segmentation, outperforming the U-Net or the level set model alone.


Image segmentation Automation image annotation Level set U-Net Convolutional networks 



This work was supported in part by the Science and Technology Service Industry project of Sichuan under 2019GFW126, Key R&D project of Sichuan under 2019ZDYF2790.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Miao Gou
    • 1
  • Yunbo Rao
    • 1
    Email author
  • Minglu Zhang
    • 1
  • Jianxun Sun
    • 2
  • Keyang Cheng
    • 3
  1. 1.School of Information and Software EngineeringUniversity of Electronic Science and Technology of ChinaChengduPeople’s Republic of China
  2. 2.West China School of StomatologySichuan UniversityChengduPeople’s Republic of China
  3. 3.School of Computer Science and Telecommunications EngineeringJiangsu UniversityZhenjiangPeople’s Republic of China

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