Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

10.3791/58305 ◽  
2018 ◽  
Author(s):  
Jonghee Yoon ◽  
YoungJu Jo ◽  
Young Seo Kim ◽  
Yeongjin Yu ◽  
Jiyeon Park ◽  
...  
Keyword(s):  
Machine Learning ◽  
Three Dimensional ◽  
Phase Imaging ◽  
Label Free ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase

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 Measuring three-dimensional dynamics of platelet activation using 3-D quantitative phase imaging

2019 ◽  
Author(s):  
SangYun Lee ◽  
Seongsoo Jang ◽  
YongKeun Park
Keyword(s):  
Platelet Activation ◽  
Three Dimensional ◽  
Dry Mass ◽  
Phase Imaging ◽  
Label Free ◽  
Quantitative Phase Imaging ◽  
Biochemical Alterations ◽  
Quantitative Phase ◽  
Before And After ◽  
Quantitative Manner

AbstractPlatelets, or thrombocytes, are anucleated tiny blood cells with an indispensable contribution to the hemostatic properties of whole blood, detecting injured sites at the surface of blood vessels and forming blood clots. Here, we quantitatively and non-invasively investigated the morphological and biochemical alterations of individual platelets during activation in the absence of exogenous agents by employing 3-D quantitative phase imaging (QPI). By reconstructing 3-D refractive index (RI) tomograms of individual platelets, we investigated alterations in platelet activation before and after the administration of various platelet agonists. Our results showed that while the integrity of collagen-stimulated platelets was preserved despite the existence of a few degranulated platelets with developed pseudopods, platelets stimulated by thrombin or thrombin receptor-activating peptide (TRAP) exhibited significantly lower cellular concentration and dry mass than did resting platelets. Our work provides a means to systematically investigate drug-respondents of individual platelets in a label-free and quantitative manner, and open a new avenue to the study of the activation of platelets.Abstract Figure

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 Lensless Three-Dimensional Quantitative Phase Imaging Using Phase Retrieval Algorithm

2020 ◽  
Vol 6 (9) ◽  
pp. 99 ◽  
Author(s):  
Vijayakumar Anand ◽  
Tomas Katkus ◽  
Denver P. Linklater ◽  
Elena P. Ivanova ◽  
Saulius Juodkazis
Keyword(s):  
Phase Retrieval ◽  
Three Dimensional ◽  
Retrieval Algorithm ◽  
Phase Imaging ◽  
Label Free ◽  
Quantitative Phase Imaging ◽  
Single Plane ◽  
Quantitative Phase ◽  
Web Camera ◽  
Phase Retrieval Algorithm

Quantitative phase imaging (QPI) techniques are widely used for the label-free examining of transparent biological samples. QPI techniques can be broadly classified into interference-based and interferenceless methods. The interferometric methods which record the complex amplitude are usually bulky with many optical components and use coherent illumination. The interferenceless approaches which need only the intensity distribution and works using phase retrieval algorithms have gained attention as they require lesser resources, cost, space and can work with incoherent illumination. With rapid developments in computational optical techniques and deep learning, QPI has reached new levels of applications. In this tutorial, we discuss one of the basic optical configurations of a lensless QPI technique based on the phase-retrieval algorithm. Simulative studies on QPI of thin, thick, and greyscale phase objects with assistive pseudo-codes and computational codes in Octave is provided. Binary phase samples with positive and negative resist profiles were fabricated using lithography, and a single plane and two plane phase objects were constructed. Light diffracted from a point object is modulated by phase samples and the corresponding intensity patterns are recorded. The phase retrieval approach is applied for 2D and 3D phase reconstructions. Commented codes in Octave for image acquisition and automation using a web camera in an open source operating system are provided.

scholarly journals Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells

2018 ◽  
Author(s):  
Geon Kim ◽  
YoungJu Jo ◽  
Hyungjoo Cho ◽  
Hyun-seok Min ◽  
YongKeun Park
Keyword(s):  
Machine Learning ◽  
Red Blood Cells ◽  
Blood Cells ◽  
Single Cells ◽  
Deficiency Anemia ◽  
High Dimensional ◽  
Phase Imaging ◽  
Label Free ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase

We present a rapid and label-free method for hematologic screening for diseases and syndromes, utilizing quantitative phase imaging (QPI) and machine learning. We aim to establish an efficient blood examination framework that does not suffer from the drawbacks of conventional blood assays, which are incapable of profiling single cells or using labeling procedures. Our method involves the synergistic employment of QPI and machine learning. The high-dimensional refractive index information arising from the QPI-based profiling of single red blood cells is processed to screen for diseases and syndromes using machine learning, which can utilize high-dimensional data beyond the human level. Accurate screening for iron-deficiency anemia, reticulocytosis, hereditary spherocytosis, and diabetes mellitus is demonstrated (>99% accuracy) using the proposed method. Furthermore, we highlight the synergy between QPI and machine learning in the proposed method by analyzing the performance of the method.

scholarly journals Label-Free Quantitative Phase Imaging Reveals Spatial Heterogeneity of Extracellular Vesicles in Selected Colon Disorders

2021 ◽  
Author(s):  
Łukasz Zadka ◽  
Igor Buzalewicz ◽  
Agnieszka Ulatowska-Jarża ◽  
Agnieszka Rusak ◽  
Maria Kochel ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
Extracellular Vesicles ◽  
Phase Imaging ◽  
Label Free ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase

scholarly journals Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging

2020 ◽  
Vol 25 (02) ◽  
pp. 1 ◽  
Author(s):  
Van K. Lam ◽  
Thanh C. Nguyen ◽  
Vy Bui ◽  
Byung Min Chung ◽  
Lin-Ching Chang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cancer Cells ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase ◽  
Quantitative Scoring

3D morphological and biophysical changes in a single tachyzoite and its infected cells using three‐dimensional quantitative phase imaging

2020 ◽  
Vol 13 (8) ◽  
Author(s):  
Egy Rahman Firdaus ◽  
Ji‐Hoon Park ◽  
Seong‐Kyun Lee ◽  
YongKeun Park ◽  
Guang‐Ho Cha ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase ◽  
Infected Cells ◽  
Biophysical Changes
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