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Deep Learning‐Based Denoising in High‐Speed Portable Reflectance Confocal Microscopy
Author(s) -
Zhao Jingwei,
Jain Manu,
Harris Ucalene G.,
Kose Kivanc,
CurielLewandrowski Clara,
Kang Dongkyun
Publication year - 2021
Publication title -
lasers in surgery and medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.888
H-Index - 112
eISSN - 1096-9101
pISSN - 0196-8092
DOI - 10.1002/lsm.23410
Subject(s) - artificial intelligence , ground truth , noise reduction , computer science , computer vision , noise (video) , image quality , deep learning , motion blur , signal to noise ratio (imaging) , reduction (mathematics) , pattern recognition (psychology) , microscopy , image (mathematics) , optics , mathematics , physics , telecommunications , geometry
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. Study Design/Materials and Methods Content‐aware image restoration (CARE) network was trained with pairs of low‐SNR input and high‐SNR ground truth PCM images obtained from 309 distinctive regions of interest (ROIs). Low‐SNR input images were acquired from human skin in vivo at the imaging speed of 180 frames/second. The high‐SNR ground truth images were generated by registering 30 low‐SNR input images obtained from the same ROI and summing them. The CARE network was trained using the Google Colaboratory Pro platform. The denoising performance of the trained CARE network was quantitatively and qualitatively evaluated by using image pairs from 45 unseen ROIs. Results CARE denoising improved the image quality significantly, increasing similarity with the ground truth image by 1.9 times, reducing noise by 2.35 times, and increasing SNR by 7.4 dB. Banding noise, prominent in input images, was significantly reduced in CARE denoised images. CARE denoising provided quantitatively and qualitatively better noise reduction than non‐DL filtering methods. Qualitative image assessment by three confocal readers showed that CARE denoised images exhibited negligible noise more often than input images and non‐DL filtered images. Conclusions Results showed the potential of using a DL‐based method for denoising PCM images obtained at a high imaging speed. The DL‐based denoising method needs to be further trained and tested for PCM images obtained from disease‐suspicious skin lesions.

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