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Deep-learning enabled label-free bio-aerosol sensing using mobile microscopy (Conference Presentation)
Author(s): Yichen Wu; Ayfer Calis; Yi Luo; Cheng Chen; Maxwell Lutton; Yair Rivenson; Xing Lin; Hatice Ceylan Koydemir; Yibo Zhang; Hongda Wang; Zoltán Göröcs; Aydogan Ozcan
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Paper Abstract

There is an increasing but unmet need for accurate, label-free and automated bio-aerosol sensing. To address this need, we developed a high-throughput, cost-effective and portable bio-aerosol sensor based on computational microscopy and deep-learning. Our device is composed of an impactor and a lens-less digital holographic on-chip microscope. It screens air at 13 liters per minute, and captures bio-aerosols on the impactor substrate. An image sensor then records the in-line holograms of these captured bio-aerosols in real time. Using these recorded in-line holograms, the captured bio-aerosols are analyzed within a minute, facilitated by two deep convolutional neural networks (CNNs): the first CNN simultaneously performs auto-focusing and phase-recovery to reconstruct both the amplitude and phase images of each bio-aerosol with sub-micron resolution; and the second CNN performs automatic classification of the reconstructed bio-aerosols into pre-trained classes and counting their densities in air. As a proof-of-concept, we demonstrated reconstruction and label-free sensing of five different types of bio-aerosols: Bermuda grass pollen, oak tree pollen, ragweed pollen, Aspergillus spore, and Alternaria spore. These bio-aerosols form some of the most common allergens in air. Using our mobile bio-aerosol sensor, we achieved ~94% precision and recall in differentiating these bio-aerosols without the use of any labeling. We also demonstrated successful sensing of oak tree pollens in the field using our mobile device. To the best of our knowledge, this is the first demonstration of automated label-free sensing of bio-aerosols using a portable device, which is enabled by computational microscopy and deep-learning. It has broad applications in label-free bio-aerosol sensing and air-quality monitoring.

Paper Details

Date Published: 4 March 2019
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Proc. SPIE 10890, Label-free Biomedical Imaging and Sensing (LBIS) 2019, 108901Z (4 March 2019); doi: 10.1117/12.2507591
Show Author Affiliations
Yichen Wu, Univ. of California, Los Angeles (United States)
Ayfer Calis, Univ. of California, Los Angeles (United States)
Yi Luo, Univ. of California, Los Angeles (United States)
Cheng Chen, Univ. of California, Los Angeles (United States)
Maxwell Lutton, Univ. of California, Los Angeles (United States)
Yair Rivenson, Univ. of California, Los Angeles (United States)
Xing Lin, Univ. of California, Los Angeles (United States)
Hatice Ceylan Koydemir, Univ. of California, Los Angeles (United States)
Yibo Zhang, Univ. of California, Los Angeles (United States)
Hongda Wang, Univ. of California, Los Angeles (United States)
Zoltán Göröcs, Univ. of California, Los Angeles (United States)
Aydogan Ozcan, Univ. of California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 10890:
Label-free Biomedical Imaging and Sensing (LBIS) 2019
Natan T. Shaked; Oliver Hayden, Editor(s)

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