Boundary-Preserved Deep Denoising of Stochastic Resonance Enhanced Multiphoton Images

Objective: With the rapid growth of high-speed deep-tissue imaging in biomedical research, there is an urgent need to develop a robust and effective denoising method to retain morphological features for further texture analysis and segmentation. Conventional denoising filters and models can easily s...

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Published in:	IEEE journal of translational engineering in health and medicine Vol. 10; p. 1
Main Authors:	Niu, Sheng-Yong, Guo, Lun-Zhang, Li, Yue, Zhang, Zhiming, Wang, Tzung-Dau, Liu, Kai-Chun, Li, You-Jin, Tsao, Yu, Liu, Tzu-Ming
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Artificial neural networks Background noise Boundaries Deep Denoising Autoencoder Fluorescence Harmonic generations Image acquisition Image contrast Image enhancement Image quality Image segmentation Imaging Microprocessors Microscopy Noise reduction Optical filters Photonics Photons Signal to noise ratio Similarity Stochastic resonance Third harmonic generation Three-photon fluorescence
ISSN:	2168-2372, 2168-2372
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Abstract	Objective: With the rapid growth of high-speed deep-tissue imaging in biomedical research, there is an urgent need to develop a robust and effective denoising method to retain morphological features for further texture analysis and segmentation. Conventional denoising filters and models can easily suppress the perturbative noise in high-contrast images; however, for low photon budget multiphoton images, a high detector gain will not only boost the signals but also bring significant background noise. In such a stochastic resonance imaging regime, subthreshold signals may be detectable with the help of noise, meaning that a denoising filter capable of removing noise without sacrificing important cellular features, such as cell boundaries, is desirable. Method: We propose a convolutional neural network-based denoising autoencoder method - a fully convolutional deep denoising autoencoder (DDAE) - to improve the quality of three-photon fluorescence (3PF) and third-harmonic generation (THG) microscopy images. Results: The average of 200 acquired images of a given location served as the low-noise answer for the DDAE training. Compared with other conventional denoising methods, our DDAE model shows a better signal-to-noise ratio (28.86 and 21.66 for 3PF and THG, respectively), structural similarity (0.89 and 0.70 for 3PF and THG, respectively), and preservation of the nuclear or cellular boundaries (F1-score of 0.662 and 0.736 for 3PF and THG, respectively). It shows that DDAE is a better trade-off approach between structural similarity and preserving signal regions. Conclusions: The results of this study validate the effectiveness of the DDAE system in boundary-preserved image denoising. Clinical Impact: The proposed deep denoising system can enhance the quality of microscopic images and effectively support clinical evaluation and assessment.
AbstractList	Objective: With the rapid growth of high-speed deep-tissue imaging in biomedical research, there is an urgent need to develop a robust and effective denoising method to retain morphological features for further texture analysis and segmentation. Conventional denoising filters and models can easily suppress the perturbative noise in high-contrast images; however, for low photon budget multiphoton images, a high detector gain will not only boost the signals but also bring significant background noise. In such a stochastic resonance imaging regime, subthreshold signals may be detectable with the help of noise, meaning that a denoising filter capable of removing noise without sacrificing important cellular features, such as cell boundaries, is desirable. Method: We propose a convolutional neural network-based denoising autoencoder method - a fully convolutional deep denoising autoencoder (DDAE) - to improve the quality of three-photon fluorescence (3PF) and third-harmonic generation (THG) microscopy images. Results: The average of 200 acquired images of a given location served as the low-noise answer for the DDAE training. Compared with other conventional denoising methods, our DDAE model shows a better signal-to-noise ratio (28.86 and 21.66 for 3PF and THG, respectively), structural similarity (0.89 and 0.70 for 3PF and THG, respectively), and preservation of the nuclear or cellular boundaries (F1-score of 0.662 and 0.736 for 3PF and THG, respectively). It shows that DDAE is a better trade-off approach between structural similarity and preserving signal regions. Conclusions: The results of this study validate the effectiveness of the DDAE system in boundary-preserved image denoising. Clinical Impact: The proposed deep denoising system can enhance the quality of microscopic images and effectively support clinical evaluation and assessment. With the rapid growth of high-speed deep-tissue imaging in biomedical research, there is an urgent need to develop a robust and effective denoising method to retain morphological features for further texture analysis and segmentation. Conventional denoising filters and models can easily suppress the perturbative noise in high-contrast images; however, for low photon budget multiphoton images, a high detector gain will not only boost the signals but also bring significant background noise. In such a stochastic resonance imaging regime, subthreshold signals may be detectable with the help of noise, meaning that a denoising filter capable of removing noise without sacrificing important cellular features, such as cell boundaries, is desirable.OBJECTIVEWith the rapid growth of high-speed deep-tissue imaging in biomedical research, there is an urgent need to develop a robust and effective denoising method to retain morphological features for further texture analysis and segmentation. Conventional denoising filters and models can easily suppress the perturbative noise in high-contrast images; however, for low photon budget multiphoton images, a high detector gain will not only boost the signals but also bring significant background noise. In such a stochastic resonance imaging regime, subthreshold signals may be detectable with the help of noise, meaning that a denoising filter capable of removing noise without sacrificing important cellular features, such as cell boundaries, is desirable.We propose a convolutional neural network-based denoising autoencoder method - a fully convolutional deep denoising autoencoder (DDAE) - to improve the quality of three-photon fluorescence (3PF) and third-harmonic generation (THG) microscopy images.METHODWe propose a convolutional neural network-based denoising autoencoder method - a fully convolutional deep denoising autoencoder (DDAE) - to improve the quality of three-photon fluorescence (3PF) and third-harmonic generation (THG) microscopy images.The average of 200 acquired images of a given location served as the low-noise answer for the DDAE training. Compared with other conventional denoising methods, our DDAE model shows a better signal-to-noise ratio (28.86 and 21.66 for 3PF and THG, respectively), structural similarity (0.89 and 0.70 for 3PF and THG, respectively), and preservation of the nuclear or cellular boundaries (F1-score of 0.662 and 0.736 for 3PF and THG, respectively). It shows that DDAE is a better trade-off approach between structural similarity and preserving signal regions.RESULTSThe average of 200 acquired images of a given location served as the low-noise answer for the DDAE training. Compared with other conventional denoising methods, our DDAE model shows a better signal-to-noise ratio (28.86 and 21.66 for 3PF and THG, respectively), structural similarity (0.89 and 0.70 for 3PF and THG, respectively), and preservation of the nuclear or cellular boundaries (F1-score of 0.662 and 0.736 for 3PF and THG, respectively). It shows that DDAE is a better trade-off approach between structural similarity and preserving signal regions.The results of this study validate the effectiveness of the DDAE system in boundary-preserved image denoising.CONCLUSIONSThe results of this study validate the effectiveness of the DDAE system in boundary-preserved image denoising.The proposed deep denoising system can enhance the quality of microscopic images and effectively support clinical evaluation and assessment.CLINICAL IMPACTThe proposed deep denoising system can enhance the quality of microscopic images and effectively support clinical evaluation and assessment.
Author	Guo, Lun-Zhang Zhang, Zhiming Liu, Kai-Chun Li, You-Jin Liu, Tzu-Ming Niu, Sheng-Yong Li, Yue Tsao, Yu Wang, Tzung-Dau
AuthorAffiliation	Department of Electrical Engineering Chung Yuan Christian University 34900 Taoyuan 32023 Taiwan Institute of Translational Medicine, Faculty of Health Sciences & Ministry of Education Frontiers Science Center for Precision Oncology, University of Macau, Taipa 59193 Macau China Research Center for Information Technology Innovation (CITI) Academia Sinica 38017 Taipei 11529 Taiwan Cardiovascular Center and Division of Cardiology Department of Internal Medicine College of Medicine, National Taiwan University Hospital 38006 Taipei 10002 Taiwan Department of Computer Science and Engineering University of California San Diego 8784 San Diego CA 92093 USA Department of Biomedical Engineering National Taiwan University 33561 Taipei 10617 Taiwan
AuthorAffiliation_xml	– name: Department of Biomedical Engineering National Taiwan University 33561 Taipei 10617 Taiwan – name: Department of Electrical Engineering Chung Yuan Christian University 34900 Taoyuan 32023 Taiwan – name: Cardiovascular Center and Division of Cardiology Department of Internal Medicine College of Medicine, National Taiwan University Hospital 38006 Taipei 10002 Taiwan – name: Department of Computer Science and Engineering University of California San Diego 8784 San Diego CA 92093 USA – name: Institute of Translational Medicine, Faculty of Health Sciences & Ministry of Education Frontiers Science Center for Precision Oncology, University of Macau, Taipa 59193 Macau China – name: Research Center for Information Technology Innovation (CITI) Academia Sinica 38017 Taipei 11529 Taiwan
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Snippet	Objective: With the rapid growth of high-speed deep-tissue imaging in biomedical research, there is an urgent need to develop a robust and effective denoising... With the rapid growth of high-speed deep-tissue imaging in biomedical research, there is an urgent need to develop a robust and effective denoising method to...
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SubjectTerms	Artificial neural networks Background noise Boundaries Deep Denoising Autoencoder Fluorescence Harmonic generations Image acquisition Image contrast Image enhancement Image quality Image segmentation Imaging Microprocessors Microscopy Noise reduction Optical filters Photonics Photons Signal to noise ratio Similarity Stochastic resonance Third harmonic generation Three-photon fluorescence
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Title	Boundary-Preserved Deep Denoising of Stochastic Resonance Enhanced Multiphoton Images
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