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Panoptic Segmentation on Panoramic Radiographs: Deep Learning-Based Segmentation of Various Structures Including

Jun-Young Cha1, Hyung-In Yoon1, In-Sung Yeo1

  • 1Department of Prosthodontics, School of Dentistry and Dental Research Institute, Seoul National University, Daehak-ro 101, Jongro-gu, Seoul 03080, Korea.

Journal of Clinical Medicine
|July 2, 2021
PubMed
Summary

This study introduces an AI model for panoptic segmentation of panoramic radiographs. The deep learning approach accurately identifies dental structures, aiding in diagnosis and treatment planning.

Keywords:
artificial intelligencedeep learningdental panoramic radiographinstance segmentationmachine learningobject detectionpanoptic segmentationsemantic segmentation

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Area of Science:

  • Dentistry
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Panoramic radiographs (orthopantomograms) are essential dental diagnostic tools.
  • Automated detection of diverse structures in these images is challenging due to variations in size and shape.
  • Existing segmentation methods struggle with the complexity of structures in panoramic radiographs.

Purpose of the Study:

  • To apply panoptic segmentation, integrating instance and semantic segmentation, to panoramic radiographs.
  • To develop and evaluate a deep neural network for segmenting key anatomical structures and dental elements.
  • To improve automated analysis of dental images for clinical applications.

Main Methods:

  • Utilized a state-of-the-art deep neural network for panoptic segmentation.
  • Trained the model to segment maxillary sinus, maxilla, mandible, mandibular canal, normal teeth, treated teeth, and dental implants.
  • Implemented individual object classification for tooth and implant classes, distinguishing from semantic segmentation.
  • Evaluated performance using panoptic quality, segmentation quality, recognition quality, IoU, and instance-level IoU.

Main Results:

  • The deep learning-based AI model successfully performed panoptic segmentation on panoramic radiographs.
  • Accurate segmentation was achieved for structures including the maxillary sinus and mandibular canal.
  • Individual classification of teeth and implants demonstrated the model's ability to differentiate instances.

Conclusions:

  • The developed AI model shows significant potential for automating the analysis of panoramic radiographs.
  • Panoptic segmentation offers a robust approach for segmenting complex anatomical and dental structures.
  • This automated method can assist dental practitioners in treatment planning and diagnosing maxillofacial diseases.