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Semi-supervised assisted multi-task learning for oral optical coherence tomography image segmentation and denoising.

Jinpeng Liao1,2, Tianyu Zhang1, Simon Shepherd3

  • 1School of Science and Engineering, University of Dundee, DD1 4HN, Scotland, UK.

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The Efficient Segmentation-Denoising Model (ESDM) significantly enhances optical coherence tomography (OCT) for oral tissue imaging. This deep learning model reduces scan times and improves the accuracy of oral epithelium segmentation and thickness measurement.

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Area of Science:

  • Biomedical Imaging
  • Medical Technology
  • Artificial Intelligence in Medicine

Background:

  • Optical coherence tomography (OCT) is a valuable non-invasive tool for oral mucosal tissue assessment.
  • OCT imaging faces challenges including speckle noise, motion artifacts, and difficulty distinguishing tissue layers due to similar optical properties.
  • Accurate segmentation and thickness quantification of oral epithelial layers are crucial for clinical diagnosis.

Purpose of the Study:

  • To introduce the Efficient Segmentation-Denoising Model (ESDM), a novel deep learning framework for enhancing OCT imaging of oral tissues.
  • To improve the speed and accuracy of OCT scanning and oral epithelium layer segmentation.
  • To reduce the cost and increase the diagnostic capabilities of OCT in clinical settings.

Main Methods:

  • Developed a multi-task deep learning framework (ESDM) integrating convolutional layers for local feature extraction and transformers for long-term information processing.
  • Implemented ESDM to simultaneously denoise OCT images, reduce motion artifacts, and segment oral epithelium layers.
  • Evaluated ESDM's performance against state-of-the-art models using metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), mean Dice coefficient (mDice), and mean Intersection over Union (mIoU).

Main Results:

  • ESDM reduced OCT scan time from approximately 8 seconds to 2 seconds.
  • Achieved superior denoising and segmentation performance compared to existing models, with PSNR of 26.272, SSIM of 0.737, mDice of 0.972, and mIoU of 0.948.
  • Demonstrated high accuracy in quantifying oral epithelium thickness, with a mean absolute error as low as 5 µm compared to manual measurements.

Conclusions:

  • ESDM effectively enhances OCT imaging quality for oral tissues by reducing noise and artifacts.
  • The model significantly improves the accuracy and efficiency of oral epithelium segmentation and thickness measurement.
  • ESDM offers a cost-effective solution for accurate oral tissue assessment, with the potential to improve clinical diagnostic capabilities.