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Mask-Transformer-Based Networks for Teeth Segmentation in Panoramic Radiographs.

Mehreen Kanwal1, Muhammad Mutti Ur Rehman2, Muhammad Umar Farooq3

  • 1DeepChain AI&IT Technologies, Islamabad 45570, Pakistan.

Bioengineering (Basel, Switzerland)
|July 29, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel panoptic segmentation method for precise teeth segmentation in dental images, improving diagnostic accuracy by considering surrounding oral tissues. The new dual-path transformer network enhances segmentation performance and robustness.

Keywords:
mask-transformer-based networkspanoptic segmentationpanoramic radiographsteeth segmentation

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Area of Science:

  • Dentistry
  • Medical Imaging
  • Computer Vision

Background:

  • Accurate teeth segmentation is crucial for dental diagnosis and treatment planning.
  • Traditional methods often neglect the broader oral tissue context, limiting segmentation accuracy.
  • Existing segmentation techniques require improvement in performance and robustness.

Purpose of the Study:

  • To propose a novel panoptic-segmentation-based method for teeth segmentation.
  • To develop a dual-path transformer-based network architecture for instance teeth segmentation.
  • To enhance the consideration of oral tissue context in dental image analysis.

Main Methods:

  • A panoptic segmentation approach combining instance and semantic segmentation.
  • A novel dual-path transformer network architecture for instance teeth segmentation.
  • Integration of a panoptic quality (PQ) loss function for streamlined training.
  • Utilizing pixel-to-memory feedback, pixel-to-pixel self-attention, and memory-to-pixel/memory-to-memory self-attention mechanisms.

Main Results:

  • The proposed model achieved substantial improvements in teeth segmentation performance on the UFBA-UESC Dental Image dataset.
  • The method surpassed existing state-of-the-art techniques in segmentation accuracy and robustness.
  • The dual-path transformer architecture effectively integrated multi-scale features and facilitated bi-directional communication.

Conclusions:

  • The developed panoptic segmentation method represents a significant advancement in teeth segmentation technology.
  • This approach offers a more comprehensive understanding of oral structures by including surrounding tissues.
  • The findings contribute to improved diagnostic capabilities and treatment planning in dentistry.