High throughput detection and quantification of <i>Giardia lamblia</i> cysts using holographic imaging flow-cytometry and deep learning
ORAL
Abstract
Annually >200 million people contract giardiasis, a diarrheal illness caused by Giardia lamblia, a microscopic waterborne parasite. To provide a cost-effective water screening tool, we created a field-portable holographic imaging flow-cytometer that can acquire in-focus phase and amplitude images of microscopic objects in water samples with a half-pitch resolution of <2µm and a liquid throughput of 100 mL/h. This computational imaging cytometer is controlled by a laptop, which is used to segment and reconstruct all the microscopic objects within the flow and can in real-time identify and count Giardia lamblia cysts using a trained convolutional neural network, achieving a detection limit of <10 cysts per 50 mL. This unique device is cost effective, compact (19 × 19 × 16 cm), lightweight (1.6 kg) and is entirely label-free, making it highly suitable for testing of drinking water supplies or for monitoring the integrity of filters in water treatment systems.
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Presenters
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Zoltan Gorocs
University of California, Los Angeles
Authors
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Zoltan Gorocs
University of California, Los Angeles
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David Baum
University of California, Los Angeles
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Fang Song
University of California, Los Angeles
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Kevin de Haan
University of California, Los Angeles
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Hatice Ceylan Koydemir
University of California, Los Angeles
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Yunzhe Qui
University of California, Los Angeles
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Zilin Cai
University of California, Los Angeles
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Thamira Skandakumar
University of California, Los Angeles
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Spencer Peterman
University of California, Los Angeles
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Miu Tamamitsu
University of California, Los Angeles
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Aydogan Ozcan
University of California, Los Angeles, Electrical and Computer Engineering, University of California, Los Angeles