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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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Different contact imaging systems without optical lens.(A) Static contact imaging system. (B) Microfluidic contact imaging system with capillary flow. (C) The proposed microfluidic contact-imaging cytometer system with continuous flow: (C1) bonding process; (C2) overall system structure.
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pone-0104539-g001: Different contact imaging systems without optical lens.(A) Static contact imaging system. (B) Microfluidic contact imaging system with capillary flow. (C) The proposed microfluidic contact-imaging cytometer system with continuous flow: (C1) bonding process; (C2) overall system structure.

Mentions: However, the captured images of microfluidic flowing cells are intrinsically in low-resolution (LR) with loss of details in cell morphology information since there is no optical lens for the flowing samples. As shown in Fig. 1(A), one Lensless Ultra wide-field Cell monitoring Array platform based on Shadow imaging (LUCAS) system is demonstrated for cell counting application [19]. To distinguish different cell types, the cell intensity distribution pattern of raw LR shadow or holographic shadow images are used [19], [22]. The cells to be imaged are statically placed in between cover slides above the image sensor array. Thus, without continuously flowing microfluidic, the total solution volume is limited in each test. In [17]–[18], a multi-frame sub-pixel resolving super-resolution (SR) processing is proposed with a high-resolution (HR) cell image recovered by capturing a large number (40 to 100) of subpixel-shifted LR cell images. As shown in Fig. 1(B), in order to capture subpixel motions in multiple frames, a drop-and-flow is employed to maintain the low flowing speed, usually driven by capillary or electroosmotic flow for precise movement control. Moreover, the storage of multiple cell images to recover one SR image consumes huge hardware resource. Both problems limit the throughput when counting multiple continuously flowing cells.


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)

Different contact imaging systems without optical lens.(A) Static contact imaging system. (B) Microfluidic contact imaging system with capillary flow. (C) The proposed microfluidic contact-imaging cytometer system with continuous flow: (C1) bonding process; (C2) overall system structure.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0104539-g001: Different contact imaging systems without optical lens.(A) Static contact imaging system. (B) Microfluidic contact imaging system with capillary flow. (C) The proposed microfluidic contact-imaging cytometer system with continuous flow: (C1) bonding process; (C2) overall system structure.
Mentions: However, the captured images of microfluidic flowing cells are intrinsically in low-resolution (LR) with loss of details in cell morphology information since there is no optical lens for the flowing samples. As shown in Fig. 1(A), one Lensless Ultra wide-field Cell monitoring Array platform based on Shadow imaging (LUCAS) system is demonstrated for cell counting application [19]. To distinguish different cell types, the cell intensity distribution pattern of raw LR shadow or holographic shadow images are used [19], [22]. The cells to be imaged are statically placed in between cover slides above the image sensor array. Thus, without continuously flowing microfluidic, the total solution volume is limited in each test. In [17]–[18], a multi-frame sub-pixel resolving super-resolution (SR) processing is proposed with a high-resolution (HR) cell image recovered by capturing a large number (40 to 100) of subpixel-shifted LR cell images. As shown in Fig. 1(B), in order to capture subpixel motions in multiple frames, a drop-and-flow is employed to maintain the low flowing speed, usually driven by capillary or electroosmotic flow for precise movement control. Moreover, the storage of multiple cell images to recover one SR image consumes huge hardware resource. Both problems limit the throughput when counting multiple continuously flowing cells.

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