Label-free quantitative imaging of lipid droplets using quantitative phase imaging techniques

2014 ◽  
Author(s):  
Seo Eun Lee ◽  
Kyoohyun Kim ◽  
Jonghee Yoon ◽  
Ji Han Heo ◽  
HyunJoo Park ◽  
...  
Keyword(s):  
Lipid Droplets ◽  
Imaging Techniques ◽  
Quantitative Imaging ◽  
Phase Imaging ◽  
Label Free ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase

scholarly journals Visualization and label-free quantification of microfluidic mixing using quantitative phase imaging

2017 ◽  
Author(s):  
GwangSik Park ◽  
Dongsik Han ◽  
GwangSu Kim ◽  
Seungwoo Shin ◽  
Kyoohyun Kim ◽  
...  
Keyword(s):  
Chemical Engineering ◽  
Imaging Techniques ◽  
Quantitative Imaging ◽  
Quantitative Information ◽  
Microfluidic Channels ◽  
Phase Imaging ◽  
Label Free ◽  
Quantitative Phase Imaging ◽  
Microfluidic Mixing ◽  
Quantitative Phase

Microfluidic mixing plays a key role in various fields, including biomedicine and chemical engineering. To date, although various approaches for imaging microfluidic mixing have been proposed, they provide only quantitative imaging capability and require for exogenous labeling agents. Quantitative phase imaging techniques, however, circumvent these problems and offer label-free quantitative information about concentration maps of microfluidic mixing. We present the quantitative phase imaging of microfluidic mixing in various types of PDMS microfluidic channels with different geometries; the feasibility of the present method was validated by comparing it with the results obtained by theoretical calculation based on Fick’s law.

scholarly journals Deep learning classification of lipid droplets in quantitative phase images

2020 ◽  
Author(s):  
L. Sheneman ◽  
G. Stephanopoulos ◽  
A. E. Vasdekis
Keyword(s):  
Machine Learning ◽  
Lipid Droplets ◽  
Phase Imaging ◽  
Label Free ◽  
Quantitative Phase Imaging ◽  
Learning Methods ◽  
Quantitative Phase ◽  
Machine Learning Methods ◽  
Phase Images

AbstractWe report the application of supervised machine learning to the automated classification of lipid droplets in label-free, quantitative-phase images. By comparing various machine learning methods commonly used in biomedical imaging and remote sensing, we found convolutional neural networks to outperform others, both quantitatively and qualitatively. We describe our imaging approach, all machine learning methods that we implemented, and their performance in computational requirements, training resource needs, and accuracy. Overall, our results indicate that quantitative-phase imaging coupled to machine learning enables accurate lipid droplet classification in single living cells. As such, the present paradigm presents an excellent alternative of the more common fluorescent and Raman imaging modalities by enabling label-free, ultra-low phototoxicity and deeper insight into the thermodynamics of metabolism of single cells.Author SummaryRecently, quantitative-phase imaging (QPI) has demonstrated the ability to elucidate novel parameters of cellular physiology and metabolism without the need for fluorescent staining. Here, we apply label-free, low photo-toxicity QPI to yeast cells in order to identify lipid droplets (LDs), an important organelle with key implications in human health and biofuel development. Because QPI yields low specificity, we explore the use of modern machine learning methods to rapidly identify intracellular LDs with high discriminatory power and accuracy. In recent years, machine learning has demonstrated exceptional abilities to recognize and segment objects in biomedical imaging, remote sensing, and other areas. Trained machine learning classifiers can be combined with QPI within high-throughput analysis pipelines, allowing for efficient and accurate identification and quantification of cellular components. Non-invasive, accurate and high-throughput classification of these organelles will accelerate research and improve our understanding of cellular functions with beneficial applications in biofuels, biomedicine, and more.

scholarly journals Variable Wavefront Curvature Phase Retrieval Compared to Off-Axis Holography and Its Useful Application to Support Intraoperative Tissue Discrimination

2018 ◽  
Vol 8 (11) ◽  
pp. 2147 ◽  
Author(s):  
Daniel Claus ◽  
Jörg Hennenlotter ◽  
Qi Liting ◽  
Giancarlo Pedrini ◽  
Arnulf Stenzl ◽  
...  
Keyword(s):  
Phase Retrieval ◽  
Imaging Techniques ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Temperature Drift ◽  
Retrieval Method ◽  
Computing Power ◽  
Quantitative Phase ◽  
Air Turbulence ◽  
Tissue Discrimination

Quantitative phase imaging can reveal morphological features without having to stain the biological sample. This property has important implications for intraoperative applications since the time spent during histopathology can be reduced from a few minutes to a few seconds. However, most common quantitative phase imaging techniques are based on the interferometric principle, which makes them more prone to disturbing environmental influences, such as temperature drift and air turbulence. In the last decade, with the advance of computing power, many different iterative quantitative phase imaging techniques, which only require the recording of the diffracted wavefield, and therefore offer increased robustness towards environmental disturbances, have been proposed. These are particularly well-suited for the application outside the well-controlled lab environment such as an operating theatre. The optical performance of our developed iterative phase retrieval method based on variable wavefront curvature will be evaluated by reference to off-axis digital holography and applied for intraoperative discrimination of tissue.

scholarly journals Quantitative Phase Imaging Techniques for the Study of Cell Pathophysiology: From Principles to Applications

Sensors ◽  
2013 ◽  
Vol 13 (4) ◽  
pp. 4170-4191 ◽  
Author(s):  
KyeoReh Lee ◽  
Kyoohyun Kim ◽  
Jaehwang Jung ◽  
JiHan Heo ◽  
Sangyeon Cho ◽  
...  
Keyword(s):  
Imaging Techniques ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase
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