scholarly journals Holo-UNet: hologram-to-hologram neural network restoration for high fidelity low light quantitative phase imaging of live cells

2020 ◽  
Vol 11 (10) ◽  
pp. 5478
Author(s):  
Zhiduo Zhang ◽  
Yujie Zheng ◽  
Tienan Xu ◽  
Avinash Upadhya ◽  
Yean Jin Lim ◽  
...  
Keyword(s):  
Neural Network ◽  
Live Cells ◽  
High Fidelity ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Low Light ◽  
Quantitative Phase ◽  
Network Restoration

Dynamic quantitative phase imaging based on Ynet-ConvLSTM neural network

2022 ◽  
Vol 150 ◽  
pp. 106833
Author(s):  
Shengyu Lu ◽  
Yong Tian ◽  
Qinnan Zhang ◽  
Xiaoxu Lu ◽  
Jindong Tian
Keyword(s):  
Neural Network ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase

scholarly journals Integrative quantitative-phase and airy light-sheet imaging

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
N. R. Subedi ◽  
P. S. Jung ◽  
E. L. Bredeweg ◽  
S. Nemati ◽  
S. E. Baker ◽  
...  
Keyword(s):  
Evolutionary Biology ◽  
Imaging System ◽  
Light Sheet ◽  
Live Cells ◽  
Phase Imaging ◽  
Label Free ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase ◽  
Light Sheet Microscopy ◽  
Order Of Magnitude

AbstractLight-sheet microscopy enables considerable speed and phototoxicity gains, while quantitative-phase imaging confers label-free recognition of cells and organelles, and quantifies their number-density that, thermodynamically, is more representative of metabolism than size. Here, we report the fusion of these two imaging modalities onto a standard inverted microscope that retains compatibility with microfluidics and open-source software for image acquisition and processing. An accelerating Airy-beam light-sheet critically enabled imaging areas that were greater by more than one order of magnitude than a Gaussian beam illumination and matched exactly those of quantitative-phase imaging. Using this integrative imaging system, we performed a demonstrative multivariate investigation of live-cells in microfluidics that unmasked that cellular noise can affect the compartmental localization of metabolic reactions. We detail the design, assembly, and performance of the integrative imaging system, and discuss potential applications in biotechnology and evolutionary biology.

scholarly journals High space-bandwidth in quantitative phase imaging using partially spatially coherent digital holographic microscopy and a deep neural network

2020 ◽  
Vol 28 (24) ◽  
pp. 36229
Author(s):  
Ankit Butola ◽  
Sheetal Raosaheb Kanade ◽  
Sunil Bhatt ◽  
Vishesh Kumar Dubey ◽  
Anand Kumar ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Digital Holographic Microscopy ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase ◽  
Holographic Microscopy ◽  
High Space

scholarly journals Quantitative Phase Imaging Using Deep Learning-Based Holographic Microscope

2021 ◽  
Vol 9 ◽  
Author(s):  
Jianglei Di ◽  
Ji Wu ◽  
Kaiqiang Wang ◽  
Ju Tang ◽  
Ying Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Dry Mass ◽  
Learning Technologies ◽  
Digital Holographic Microscopy ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase ◽  
Living Mouse ◽  
Digital Holograms

Digital holographic microscopy enables the measurement of the quantitative light field information and the visualization of transparent specimens. It can be implemented for complex amplitude imaging and thus for the investigation of biological samples including tissues, dry mass, membrane fluctuation, etc. Currently, deep learning technologies are developing rapidly and have already been applied to various important tasks in the coherent imaging. In this paper, an optimized structural convolution neural network PhaseNet is proposed for the reconstruction of digital holograms, and a deep learning-based holographic microscope using above neural network is implemented for quantitative phase imaging. Living mouse osteoblastic cells are quantitatively measured to demonstrate the capability and applicability of the system.

scholarly journals Quantitative phase imaging of live cells using fast Fourier phase microscopy

2007 ◽  
Vol 46 (10) ◽  
pp. 1836 ◽  
Author(s):  
Niyom Lue ◽  
Wonshik Choi ◽  
Gabriel Popescu ◽  
Takahiro Ikeda ◽  
Ramachandra R. Dasari ◽  
...  
Keyword(s):  
Live Cells ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase ◽  
Phase Microscopy
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