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光学时拉伸成像流式细胞术在压缩域中。

Optical time-stretch imaging flow cytometry in the compressed domain.

机构信息

The Institute of Technological Sciences, Wuhan University, Wuhan, China.

The Key Laboratory of Transients in Hydraulic Machinery of Ministry of Education, School of Power and Mechanical Engineering, Wuhan University, Wuhan, China.

出版信息

J Biophotonics. 2023 Aug;16(8):e202300096. doi: 10.1002/jbio.202300096. Epub 2023 May 18.

Abstract

Imaging flow cytometry based on optical time-stretch (OTS) imaging combined with a microfluidic chip attracts much attention in the large-scale single-cell analysis due to its high throughput, high precision, and label-free operation. Compressive sensing has been integrated into OTS imaging to relieve the pressure on the sampling and transmission of massive data. However, image decompression brings an extra overhead of computing power to the system, but does not generate additional information. In this work, we propose and demonstrate OTS imaging flow cytometry in the compressed domain. Specifically, we constructed a machine-learning network to analyze the cells without decompressing the images. The results show that our system enables high-quality imaging and high-accurate cell classification with an accuracy of over 99% at a compression ratio of 10%. This work provides a viable solution to the big data problem in OTS imaging flow cytometry, boosting its application in practice.

摘要

基于光时拉伸(OTS)成像的成像流式细胞术与微流控芯片结合,由于其高通量、高精度和无标记操作,在大规模单细胞分析中引起了广泛关注。压缩感知已被集成到 OTS 成像中,以缓解大量数据的采样和传输压力。然而,图像解压给系统带来了额外的计算能力开销,但不会产生额外的信息。在这项工作中,我们提出并展示了压缩域中的 OTS 成像流式细胞术。具体来说,我们构建了一个机器学习网络来分析未解压图像的细胞。结果表明,我们的系统在压缩比为 10%时能够实现高质量的成像和高精度的细胞分类,准确率超过 99%。这项工作为 OTS 成像流式细胞术的大数据问题提供了一种可行的解决方案,推动了其在实际中的应用。

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