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A contact-imaging based microfluidic cytometer with machine-learning for single-frame super-resolution processing.

Huang X, Guo J, Wang X, Yan M, Kang Y, Yu H - PLoS ONE (2014)

Bottom Line: This paper introduces a single-frame super-resolution processing with on-line machine-learning for contact images of cells.A corresponding contact-imaging based microfluidic cytometer prototype is demonstrated for cell recognition and counting.Compared with commercial flow cytometer, less than 8% error is observed for absolute number of microbeads; and 0.10 coefficient of variation is observed for cell-ratio of mixed RBC and HepG2 cells in solution.

View Article: PubMed Central - PubMed

Affiliation: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore.

ABSTRACT
Lensless microfluidic imaging with super-resolution processing has become a promising solution to miniaturize the conventional flow cytometer for point-of-care applications. The previous multi-frame super-resolution processing system can improve resolution but has limited cell flow rate and hence low throughput when capturing multiple subpixel-shifted cell images. This paper introduces a single-frame super-resolution processing with on-line machine-learning for contact images of cells. A corresponding contact-imaging based microfluidic cytometer prototype is demonstrated for cell recognition and counting. Compared with commercial flow cytometer, less than 8% error is observed for absolute number of microbeads; and 0.10 coefficient of variation is observed for cell-ratio of mixed RBC and HepG2 cells in solution.

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Comparison of concentration measurement results for 6 µm microbead solution between the developed microfluidic cytometer and the commercial flow cytometer.The average counting result of the developed microfluidic cytometer matched well with that of the commercial cytometer with 8% error.
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pone-0104539-g004: Comparison of concentration measurement results for 6 µm microbead solution between the developed microfluidic cytometer and the commercial flow cytometer.The average counting result of the developed microfluidic cytometer matched well with that of the commercial cytometer with 8% error.

Mentions: As described in the previous section, the 6 µm polystyrene microbead solution was prepared with a concentration of 100 µL−1 measured by the commercial flow cytometer. The 6 µm sample was flushed through the microfluidic channel at a flow rate of 5 µL/min by a syringe pump. Then, a series of 640 frames were captured by the CMOS image sensor for a period of one minute. The total number of microbeads was automatically counted by the developed image processing algorithm. The same process was repeated for 6 minutes, and the measured concentrations of the microbead are shown in Fig. 4. The final microbead concentration is calculated by averaging the counting results of each group, which was 91 uL−1 with only 8% error when compared with the result 99 uL−1 by the commercial flow cytometer.


A contact-imaging based microfluidic cytometer with machine-learning for single-frame super-resolution processing.

Huang X, Guo J, Wang X, Yan M, Kang Y, Yu H - PLoS ONE (2014)

Comparison of concentration measurement results for 6 µm microbead solution between the developed microfluidic cytometer and the commercial flow cytometer.The average counting result of the developed microfluidic cytometer matched well with that of the commercial cytometer with 8% error.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4128713&req=5

pone-0104539-g004: Comparison of concentration measurement results for 6 µm microbead solution between the developed microfluidic cytometer and the commercial flow cytometer.The average counting result of the developed microfluidic cytometer matched well with that of the commercial cytometer with 8% error.
Mentions: As described in the previous section, the 6 µm polystyrene microbead solution was prepared with a concentration of 100 µL−1 measured by the commercial flow cytometer. The 6 µm sample was flushed through the microfluidic channel at a flow rate of 5 µL/min by a syringe pump. Then, a series of 640 frames were captured by the CMOS image sensor for a period of one minute. The total number of microbeads was automatically counted by the developed image processing algorithm. The same process was repeated for 6 minutes, and the measured concentrations of the microbead are shown in Fig. 4. The final microbead concentration is calculated by averaging the counting results of each group, which was 91 uL−1 with only 8% error when compared with the result 99 uL−1 by the commercial flow cytometer.

Bottom Line: This paper introduces a single-frame super-resolution processing with on-line machine-learning for contact images of cells.A corresponding contact-imaging based microfluidic cytometer prototype is demonstrated for cell recognition and counting.Compared with commercial flow cytometer, less than 8% error is observed for absolute number of microbeads; and 0.10 coefficient of variation is observed for cell-ratio of mixed RBC and HepG2 cells in solution.

View Article: PubMed Central - PubMed

Affiliation: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore.

ABSTRACT
Lensless microfluidic imaging with super-resolution processing has become a promising solution to miniaturize the conventional flow cytometer for point-of-care applications. The previous multi-frame super-resolution processing system can improve resolution but has limited cell flow rate and hence low throughput when capturing multiple subpixel-shifted cell images. This paper introduces a single-frame super-resolution processing with on-line machine-learning for contact images of cells. A corresponding contact-imaging based microfluidic cytometer prototype is demonstrated for cell recognition and counting. Compared with commercial flow cytometer, less than 8% error is observed for absolute number of microbeads; and 0.10 coefficient of variation is observed for cell-ratio of mixed RBC and HepG2 cells in solution.

Show MeSH
Related in: MedlinePlus