A contact-imaging based microfluidic cytometer with machine-learning for single-frame super-resolution processing
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 lo...
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| Main Authors: | , , , , , |
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| 其他作者: | |
| 格式: | Article |
| 語言: | English |
| 出版: |
2014
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/105049 http://hdl.handle.net/10220/20408 |
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| 總結: | 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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