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.

NEW POSSIBILITIES OF STRUCTURAL MORPHOLOGICAL PECULIARITIES INVESTIGATION BY QUANTITATIVE PHASE IMAGING METHOD

2020 ◽  
pp. 133-135
Author(s):  
M. Zolotova ◽  
M. Ivashchenko ◽  
P. Ignatiev ◽  
V. Metelin ◽  
M. Talamanova
Keyword(s):  
Red Blood Cells ◽  
Blood Cells ◽  
Stress Conditions ◽  
Imaging Method ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase ◽  
Stress Influence

The possibility of quantitative phase imaging method for the assessment of structural morphological erythrocytes peculiarities under normal and stress conditions were studied. It is stated that quantitative phase imaging is an important instrument which allows to visualize red blood cells. New aspects of stress influence on functional cells morphology are defined.

scholarly journals Reagent- and actuator-free analysis of individual erythrocytes using three-dimensional quantitative phase imaging and capillary microfluidics

2021 ◽  
Author(s):  
DongHun Ryu ◽  
Hyeono Nam ◽  
Jessie Sungyun Jeon ◽  
YongKeun Park
Keyword(s):  
Blood Cells ◽  
Histopathological Examination ◽  
Point Of Care ◽  
Hemoglobin Concentration ◽  
Biochemical Properties ◽  
Phase Imaging ◽  
Sample Treatment ◽  
Label Free ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase

Histopathological examination of blood cells plays a crucial role in the diagnosis of various diseases. However, it involves time-consuming and laborious staining procedures required for microscopic review by medical experts and is not directly applicable for point-of-care diagnosis in resource-limited locations. This study reports a dilution-, actuation- and label-free method for the analysis of individual red blood cells (RBCs) using a capillary microfluidic device and quantitative phase imaging. Blood, without any sample treatment, is directly loaded into a micrometer-thick channel such that it forms a quasi-monolayer inside the channel. The morphological and biochemical properties of RBCs, including hemoglobin concentration, hemoglobin content, and corpuscular volume, were retrieved using the refractive index tomograms of individual RBCs measured using 3D quantitative phase imaging. The deformability of individual RBCs was also obtained by measuring the dynamic membrane fluctuations. The proposed framework applies to other imaging modalities and biomedical applications, facilitating rapid and cost-effective diagnosis and prognosis of diseases.

scholarly journals Invited Article: Digital refocusing in quantitative phase imaging for flowing red blood cells

APL Photonics ◽  
2018 ◽  
Vol 3 (11) ◽  
pp. 110802 ◽  
Author(s):  
Han Sang Park ◽  
Silvia Ceballos ◽  
Will J. Eldridge ◽  
Adam Wax
Keyword(s):  
Red Blood Cells ◽  
Blood Cells ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase ◽  
Digital Refocusing

Quantitative phase imaging techniques for the study of Red Blood Cells

2016 ◽  
Author(s):  
Marco Antonio Sandoval Hernández ◽  
Noel-Ivan Toto-Arellano ◽  
L. A. Bonilla Jiménez ◽  
J. A. Martínez Domínguez ◽  
Luis García Lechuga ◽  
...  
Keyword(s):  
Red Blood Cells ◽  
Blood Cells ◽  
Imaging Techniques ◽  
Phase Imaging ◽  
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.

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