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Deep Learning-Based Denoising in High-Speed Portable Reflectance Confocal Microscopy.

Jingwei Zhao1, Manu Jain2, Ucalene G Harris2

  • 1College of Optical Sciences, University of Arizona, Tucson, Arizona, 85721.

Lasers in Surgery and Medicine
|April 23, 2021
PubMed
Summary

A deep learning (DL) approach using a content-aware image restoration (CARE) network effectively reduced noise in portable confocal microscopy (PCM) images. This method enhances image quality for in vivo skin imaging, overcoming challenges from short exposure times.

Keywords:
content-aware image restoration (CARE)deep learning (DL)image denoisingportable confocal microscopy (PCM)reflectance confocal microscopy (RCM)

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Area of Science:

  • Medical Imaging
  • Optical Microscopy
  • Artificial Intelligence in Medicine

Background:

  • Portable confocal microscopy (PCM) enables in vivo visualization of human skin.
  • Short exposure times in PCM reduce motion blur but significantly decrease signal-to-noise ratio (SNR).
  • Low SNR in PCM images challenges reliable cellular feature analysis.

Purpose of the Study:

  • To evaluate a deep learning (DL)-based approach for noise reduction in low-SNR PCM images.
  • To assess the effectiveness of a content-aware image restoration (CARE) network for enhancing PCM image quality.

Main Methods:

  • A CARE network was trained using pairs of low-SNR and high-SNR PCM images from human skin in vivo.
  • High-SNR images were generated by averaging multiple low-SNR images from the same region.
  • The trained CARE network was evaluated on unseen regions for denoising performance.

Main Results:

  • CARE denoising significantly improved image quality, increasing similarity to ground truth by 1.9 times.
  • Noise was reduced by 2.35 times, and SNR increased by 7.4 dB.
  • CARE denoising outperformed non-DL filtering methods, with reduced banding noise and negligible noise in qualitative assessments.

Conclusions:

  • DL-based denoising shows potential for improving PCM images acquired at high speeds.
  • Further training and testing are needed for PCM images of disease-suspicious skin lesions.