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

• Main Authors: Huang, Xiwei, Guo, Jinhong, Wang, Xiaolong, Yan, Mei, Kang, Yuejun, Yu, Hao
• Other Authors: Kreplak, Laurent
• Format: Article
• Language: English
• Published: 2014
• Subjects: DRNTU::Engineering::Chemical engineering::Biochemical engineering
• Online Access: https://hdl.handle.net/10356/105049; http://hdl.handle.net/10220/20408
• Institution: Nanyang Technological University

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.