Quantitative phase imaging-based machine learning approaches for the analysis of adherent and suspended cells

2021 ◽  
Author(s):  
Björn Kemper ◽  
Hanna Eilers ◽  
Tilmann Klein ◽  
Klaus Brinker ◽  
Steffi Ketelhut
Keyword(s):  
Machine Learning ◽  
Phase Imaging ◽  
Learning Approaches ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase ◽  
Suspended Cells

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 Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning

2017 ◽  
Vol 22 (3) ◽  
pp. 036015 ◽  
Author(s):  
Tan H. Nguyen ◽  
Shamira Sridharan ◽  
Virgilia Macias ◽  
Andre Kajdacsy-Balla ◽  
Jonathan Melamed ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase ◽  
Gleason Grading

Pebrine diagnosis using quantitative phase imaging and machine learning

2021 ◽  
Author(s):  
P. P. Prasobhkumar ◽  
Aravind Venukumar ◽  
C. R. Francis ◽  
Sai Siva Gorthi
Keyword(s):  
Machine Learning ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase
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