Classification of blood cells and tumor cells using label-free ultrasound and photoacoustics

2015 ◽  
Vol 87 (8) ◽  
pp. 741-749 ◽  
Author(s):  
Eric M. Strohm ◽  
Michael C. Kolios
Keyword(s):  
Tumor Cells ◽  
Blood Cells ◽  
Label Free

scholarly journals Label-Free White Blood Cell Classification Using Refractive Index Tomography and Deep Learning

BME Frontiers ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
DongHun Ryu ◽  
Jinho Kim ◽  
Daejin Lim ◽  
Hyun-Seok Min ◽  
In Young Yoo ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Refractive Index ◽  
Deep Learning ◽  
Blood Cell ◽  
White Blood Cell ◽  
Blood Cells ◽  
Hematologic Malignancy ◽  
Label Free ◽  
Cell Classification

Objective and Impact Statement. We propose a rapid and accurate blood cell identification method exploiting deep learning and label-free refractive index (RI) tomography. Our computational approach that fully utilizes tomographic information of bone marrow (BM) white blood cell (WBC) enables us to not only classify the blood cells with deep learning but also quantitatively study their morphological and biochemical properties for hematology research. Introduction. Conventional methods for examining blood cells, such as blood smear analysis by medical professionals and fluorescence-activated cell sorting, require significant time, costs, and domain knowledge that could affect test results. While label-free imaging techniques that use a specimen’s intrinsic contrast (e.g., multiphoton and Raman microscopy) have been used to characterize blood cells, their imaging procedures and instrumentations are relatively time-consuming and complex. Methods. The RI tomograms of the BM WBCs are acquired via Mach-Zehnder interferometer-based tomographic microscope and classified by a 3D convolutional neural network. We test our deep learning classifier for the four types of bone marrow WBC collected from healthy donors (n=10): monocyte, myelocyte, B lymphocyte, and T lymphocyte. The quantitative parameters of WBC are directly obtained from the tomograms. Results. Our results show >99% accuracy for the binary classification of myeloids and lymphoids and >96% accuracy for the four-type classification of B and T lymphocytes, monocyte, and myelocytes. The feature learning capability of our approach is visualized via an unsupervised dimension reduction technique. Conclusion. We envision that the proposed cell classification framework can be easily integrated into existing blood cell investigation workflows, providing cost-effective and rapid diagnosis for hematologic malignancy.

On the Design of a DLD-DEP Device for Separation of Circulating Tumor Cells in Blood

2020 ◽  
Author(s):  
Mehdi Rahmati ◽  
Xiaolin Chen
Keyword(s):  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Cancer Progression ◽  
High Throughput ◽  
Blood Cells ◽  
Lateral Displacement ◽  
Uniform Electric Field ◽  
Label Free ◽  
Particle Tracing ◽  
Key Factor

Abstract Circulating Tumor Cells (CTCs), which migrate from original sites in a body to distant organs through blood, are a key factor in cancer detection. Emerging Label-free techniques owing to their inherent advantage to preserve characteristics of sorted cells and low consumption of samples can be promising to the prediction of cancer progression and metastasis research. Deterministic Lateral Displacement (DLD) is one of the label-free separation techniques employing a specific arrangement of micro-posts for continuous separation of suspended cells in a buffer based on the size of cells. Separation based solely on size is challenging since the size distributions of CTCs might overlap with those of normal blood cells. To address this problem, DLD can be combined with dielectrophoresis (DEP) technique which is the phenomenon of particle movement in a non-uniform electric field owing to the polarization effect. Although, DLD devices employ the laminar flow in low Reynolds number (Re) fluid flow due to predictability of such flow regimes, they should be improved to work in higher Re flow regime so as to attain high throughput devices. In this paper, a particle tracing simulation is developed to study the effects of different post shapes, shift fraction of micropost arrays, and dielectrophoresis forces on separation of CTCs from peripheral blood cells. Our numerical model and results provide a groundwork for design and fabrication of high-throughput DLD-DEP devices for improvement of CTC separation.

Acoustofluidic, Label-Free Separation and Simultaneous Concentration of Rare Tumor Cells from White Blood Cells

2015 ◽  
Vol 87 (18) ◽  
pp. 9322-9328 ◽  
Author(s):  
Maria Antfolk ◽  
Cecilia Magnusson ◽  
Per Augustsson ◽  
Hans Lilja ◽  
Thomas Laurell
Keyword(s):  
Tumor Cells ◽  
Blood Cells ◽  
White Blood Cells ◽  
Rare Tumor ◽  
Label Free

scholarly journals Label-free rapid identification of tumor cells and blood cells with silver film SERS substrate

2018 ◽  
Vol 26 (25) ◽  
pp. 33044 ◽  
Author(s):  
Y. J. Zhang ◽  
Q. Y. Zeng ◽  
L. F. Li ◽  
M. N. Qi ◽  
Q. C. Qi ◽  
...  
Keyword(s):  
Tumor Cells ◽  
Blood Cells ◽  
Silver Film ◽  
Rapid Identification ◽  
Sers Substrate ◽  
Label Free

scholarly journals Membrane capacitance of thousands of single white blood cells

2017 ◽  
Vol 14 (137) ◽  
pp. 20170717 ◽  
Author(s):  
Ke Wang ◽  
Chun-Chieh Chang ◽  
Tzu-Keng Chiu ◽  
Xiaoting Zhao ◽  
Deyong Chen ◽  
...  
Keyword(s):  
Blood Cells ◽  
Single Cells ◽  
Statistical Significance ◽  
Gradient Centrifugation ◽  
White Blood Cells ◽  
Membrane Capacitance ◽  
Label Free ◽  
Specific Membrane ◽  
Total Membrane

As label-free biomarkers, the electrical properties of single cells are widely used for cell type classification and cellular status evaluation. However, as intrinsic cellular electrical markers, previously reported membrane capacitances (e.g. specific membrane capacitance C spec and total membrane capacitance C mem ) of white blood cells were derived from tens of single cells, lacking statistical significance due to low cell numbers. In this study, white blood cells were first separated into granulocytes and lymphocytes by density gradient centrifugation and were then aspirated through a microfluidic constriction channel to characterize both C spec and C mem . Thousands of granulocytes ( n cell = 3327) and lymphocytes ( n cell = 3302) from 10 healthy blood donors were characterized, resulting in C spec values of 1.95 ± 0.22 µF cm −2 versus 2.39 ± 0.39 µF cm −2 and C mem values of 6.81 ± 1.09 pF versus 4.63 ± 0.57 pF. Statistically significant differences between granulocytes and lymphocytes were located for both C spec and C mem . In addition, neural network-based pattern recognition was used to classify white blood cells, producing successful classification rates of 78.1% for C spec and 91.3% for C mem , respectively. These results indicate that as intrinsic bioelectrical markers, membrane capacitances may contribute to the classification of white blood cells.

Label-free metabolic clustering through unsupervised pixel classification of multiparametric fluorescent images

2020 ◽  
Author(s):  
Giada Bianchetti ◽  
Fabio Ciccarone ◽  
Maria Rosa Ciriolo ◽  
Marco De Spirito ◽  
Giovambattista Pani ◽  
...  
Keyword(s):  
Label Free ◽  
Pixel Classification

Label-free non-invasive classification of rice seeds using optical coherence tomography assisted with deep neural network

2021 ◽  
Vol 137 ◽  
pp. 106861
Author(s):  
Deepa Joshi ◽  
Ankit Butola ◽  
Sheetal Raosaheb Kanade ◽  
Dilip K. Prasad ◽  
S.V. Amitha Mithra ◽  
...  
Keyword(s):  
Neural Network ◽  
Optical Coherence Tomography ◽  
Deep Neural Network ◽  
Optical Coherence ◽  
Label Free ◽  
Non Invasive ◽  
Rice Seeds
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