流式细胞术
流量(数学)
计算机科学
生物医学工程
医学
物理
免疫学
机械
作者
Kangrui Huang,Hiroki Matsumura,Yaqi Zhao,Maik Herbig,Dan Yuan,Yohei Mineharu,Jeffrey Harmon,Justin Findinier,Mai Yamagishi,Shinsuke Ohnuki,Nao Nitta,Arthur Grossman,Yoshikazu Ohya,Hideharu Mikami,Akihiro Isozaki,Keisuke Goda
出处
期刊:Lab on a Chip
[Royal Society of Chemistry]
日期:2022-01-01
卷期号:22 (5): 876-889
被引量:29
摘要
Imaging flow cytometry (IFC) has become a powerful tool for diverse biomedical applications by virtue of its ability to image single cells in a high-throughput manner. However, there remains a challenge posed by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present deep-learning-enhanced imaging flow cytometry (dIFC) that circumvents this trade-off by implementing an image restoration algorithm on a virtual-freezing fluorescence imaging (VIFFI) flow cytometry platform, enabling higher throughput without sacrificing sensitivity and spatial resolution. A key component of dIFC is a high-resolution (HR) image generator that synthesizes "virtual" HR images from the corresponding low-resolution (LR) images acquired with a low-magnification lens (10×/0.4-NA). For IFC, a low-magnification lens is favorable because of reduced image blur of cells flowing at a higher speed, which allows higher throughput. We trained and developed the HR image generator with an architecture containing two generative adversarial networks (GANs). Furthermore, we developed dIFC as a method by combining the trained generator and IFC. We characterized dIFC using
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