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Proceedings Paper

AI-augmented time stretch microscopy
Author(s): Ata Mahjoubfar; Claire L. Chen; Jiahao Lin; Bahram Jalali
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Paper Abstract

Cell reagents used in biomedical analysis often change behavior of the cells that they are attached to, inhibiting their native signaling. On the other hand, label-free cell analysis techniques have long been viewed as challenging either due to insufficient accuracy by limited features, or because of low throughput as a sacrifice of improved precision. We present a recently developed artificial-intelligence augmented microscope, which builds upon high-throughput time stretch quantitative phase imaging (TS-QPI) and deep learning to perform label-free cell classification with record high-accuracy. Our system captures quantitative optical phase and intensity images simultaneously by frequency multiplexing, extracts multiple biophysical features of the individual cells from these images fused, and feeds these features into a supervised machine learning model for classification. The enhanced performance of our system compared to other label-free assays is demonstrated by classification of white blood T-cells versus colon cancer cells and lipid accumulating algal strains for biofuel production, which is as much as five-fold reduction in inaccuracy. This system obtains the accuracy required in practical applications such as personalized drug development, while the cells remain intact and the throughput is not sacrificed. Here, we introduce a data acquisition scheme based on quadrature phase demodulation that enables interruptionless storage of TS-QPI cell images. Our proof of principle demonstration is capable of saving 40 TB of cell images in about four hours, i.e. pictures of every single cell in 10 mL of a sample.

Paper Details

Date Published: 22 February 2017
PDF: 7 pages
Proc. SPIE 10076, High-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management II, 100760J (22 February 2017); doi: 10.1117/12.2252572
Show Author Affiliations
Ata Mahjoubfar, Univ. of California, Los Angeles (United States)
Claire L. Chen, Univ. of California, Los Angeles (United States)
California NanoSystems Institute (United States)
Jiahao Lin, Univ. of California, Los Angeles (United States)
Bahram Jalali, Univ. of California, Los Angeles (United States)
California NanoSystems Institute (United States)


Published in SPIE Proceedings Vol. 10076:
High-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management II
Kevin K. Tsia; Keisuke Goda, Editor(s)

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