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4 March 2019 Deep learning-based label-free imaging flow cytometry for on-site analysis of water samples (Conference Presentation)
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Abstract
We introduce a field-portable and cost-effective holographic imaging flow cytometer, which provides phase contrast microscopic images of the contents of water samples at a throughput of 100 ml/h. This imaging cytometer uses a high power multi-colored LED and a custom designed circuit to illuminate continuously flowing water samples with short-pulses of red, green, and blue light that are simultaneously on, thereby eliminating motion blur and making the system vibration resistant. The recorded color holograms are segmented and reconstructed in real time and are phase recovered using a deep learning-based algorithm. Weighing 1kg with the dimensions of 15.5 cm × 15 cm × 12.5 cm, our label-free imaging flow-cytometer is controlled by a laptop computer equipped with a graphical processing unit. We tested the capabilities of our field-portable device by imaging micro- and nano-plankton inside ocean water samples collected at six beaches along the California coastline. We also determined Pseudo-Nitzschia algae concentration of these samples, providing a good agreement with the measurements made by the California Department of Public Health. Our device represents 1-2 orders of magnitude reduction in the cost and size of an imaging flow cytometer compared to state-of-the-art designs, while providing a similar or better performance in terms of volumetric throughput, detection limit and imaging resolution.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zoltán S. Göröcs, Miu Tamamitsu, Vittorio Bianco, Patrick Wolf, Shounak Roy, Koyoshi Shindo, Kyrollos Yanny, Yichen Wu, Hatice Koydemir, Yair Rivenson, and Aydogan Ozcan "Deep learning-based label-free imaging flow cytometry for on-site analysis of water samples (Conference Presentation)", Proc. SPIE 10869, Optics and Biophotonics in Low-Resource Settings V, 108690K (4 March 2019); https://doi.org/10.1117/12.2508058
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