scholarly journals Quantitative phase imaging in dual-wavelength interferometry using a single wavelength illumination and deep learning

2020 ◽  
Vol 28 (19) ◽  
pp. 28140
Author(s):  
Jiaosheng Li ◽  
Qinnan Zhang ◽  
Liyun Zhong ◽  
Jindong Tian ◽  
Giancarlo Pedrini ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dual Wavelength ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase

Super-resolution quantitative phase imaging of out-of-focus images based on deep learning

2021 ◽  
Author(s):  
Xin Qian ◽  
Hao Ding ◽  
Fajing Li ◽  
Shouping Nie ◽  
Caojin Yuan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Super Resolution ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase

Single-shot higher-order transport-of-intensity quantitative phase imaging using deep learning

2021 ◽  
Vol 60 (28) ◽  
pp. 8802
Author(s):  
Naru Yoneda ◽  
Shunsuke Kakei ◽  
Koshi Komuro ◽  
Aoi Onishi ◽  
Yusuke Saita ◽  
...  
Keyword(s):  
Deep Learning ◽  
Higher Order ◽  
Phase Imaging ◽  
Single Shot ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase

scholarly journals Rapid and label-free identification of individual bacterial pathogens exploiting three-dimensional quantitative phase imaging and deep learning

2019 ◽  
Author(s):  
Geon Kim ◽  
Daewoong Ahn ◽  
Minhee Kang ◽  
YoungJu Jo ◽  
Donghun Ryu ◽  
...  
Keyword(s):  
Refractive Index ◽  
Deep Learning ◽  
Infectious Diseases ◽  
Bacterial Pathogens ◽  
Bacterial Species ◽  
Three Dimensional ◽  
Phase Imaging ◽  
Label Free ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase

ABSTRACTFor appropriate treatments of infectious diseases, rapid identification of the pathogens is crucial. Here, we developed a rapid and label-free method for identifying common bacterial pathogens as individual bacteria by using three-dimensional quantitative phase imaging and deep learning. We achieved 95% accuracy in classifying 19 bacterial species by exploiting the rich information in three-dimensional refractive index tomograms with a convolutional neural network classifier. Extensive analysis of the features extracted by the trained classifier was carried out, which supported that our classifier is capable of learning species-dependent characteristics. We also confirmed that utilizing three-dimensional refractive index tomograms was crucial for identification ability compared to two-dimensional imaging. This method, which does not require time-consuming culture, shows high feasibility for diagnosing patients with infectious diseases who would benefit from immediate and adequate antibiotic treatment.

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.

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