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

Journal: Lasers in surgery and medicine
Published Date: Apr 23, 2021

Abstract

BACKGROUND AND OBJECTIVE: Portable confocal microscopy (PCM) is a low-cost reflectance confocal microscopy technique that can visualize cellular details of human skin in vivo. When PCM images are acquired with a short exposure time to reduce motion blur and enable real-time 3D imaging, the signal-to-noise ratio (SNR) is decreased significantly, which poses challenges in reliably analyzing cellular features. In this paper, we evaluated deep learning (DL)-based approach for reducing noise in PCM images acquired with a short exposure time.

Authors

  • Jingwei Zhao
    Spine Department, Beijing Jishuitan Hospital, Beijing, China.
  • Manu Jain
    Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, New York, 10021.
  • Ucalene G Harris
    Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, New York, 10021.
  • Kivanc Kose
    Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA. Electronic address: kosek@mskcc.org.
  • Clara Curiel-Lewandrowski
    University of Arizona Cancer Center, Tucson, Arizona, 85721.
  • Dongkyun Kang
    College of Optical Sciences, University of Arizona, Tucson, Arizona, 85721.

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