Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Prerequisite Research for the Development of an End-to-End System for Automatic Tooth Segmentation: A Deep Learning-Based Reference Point Setting Algorithm

자동 치아 분할용 종단 간 시스템 개발을 위한 선결 연구: 딥러닝 기반 기준점 설정 알고리즘

  • Kyungdeok Seo (Department of Biomedical Engineering, Yonsei University) ;
  • Sena Lee (Department of Precision Medicine, Yonsei University Wonju College of Medicine) ;
  • Yongkyu Jin (diorco) ;
  • Sejung Yang (Department of Precision Medicine, Yonsei University Wonju College of Medicine)
  • 서경덕 (연세대학교 의공학과) ;
  • 이세나 (연세대학교 원주의과대학 정밀의학과) ;
  • 진용규 (주식회사 디오코) ;
  • 양세정 (연세대학교 원주의과대학 정밀의학과)
  • Received : 2023.10.04
  • Accepted : 2023.10.26
  • Published : 2023.10.31
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Abstract

In this paper, we propose an innovative approach that leverages deep learning to find optimal reference points for achieving precise tooth segmentation in three-dimensional tooth point cloud data. A dataset consisting of 350 aligned maxillary and mandibular cloud data was used as input, and both end coordinates of individual teeth were used as correct answers. A two-dimensional image was created by projecting the rendered point cloud data along the Z-axis, where an image of individual teeth was created using an object detection algorithm. The proposed algorithm is designed by adding various modules to the Unet model that allow effective learning of a narrow range, and detects both end points of the tooth using the generated tooth image. In the evaluation using DSC, Euclid distance, and MAE as indicators, we achieved superior performance compared to other Unet-based models. In future research, we will develop an algorithm to find the reference point of the point cloud by back-projecting the reference point detected in the image in three dimensions, and based on this, we will develop an algorithm to divide the teeth individually in the point cloud through image processing techniques.

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References

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