Cagatay Isil,1 Kevin De Haan,1 Hatice Ceylan Koydemir,1 Zoltán Göröcs,1 David Baum,1 Fang Song,1 Thamira Skandakumar,1 Esin Gumustekin,1 Aydogan Ozcanhttps://orcid.org/0000-0002-0717-683X1
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We present a field-portable and high-throughput imaging flow-cytometer, which performs phenotypic analysis of microalgae using image processing and deep learning. This computational cytometer weighs ~1.6kg, and captures holographic images of water samples containing microalgae, flowing in a microfluidic channel at a rate of 100mL/h. Automated analysis is performed by extracting the spatial and spectral features of the reconstructed images to automatically identify/count the target algae within the sample, using image processing and convolutional neural networks. Changes within the measured features and the composition of the microalgae can be rapidly analyzed to reveal even minute deviations from the normal state of the population.
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Cagatay Isil, Kevin De Haan, Hatice Ceylan Koydemir, Zoltán Göröcs, David Baum, Fang Song, Thamira Skandakumar, Esin Gumustekin, Aydogan Ozcan, "Label-free analysis of micro-algae populations using a high-throughput holographic imaging flow cytometer and deep learning," Proc. SPIE 11655, Label-free Biomedical Imaging and Sensing (LBIS) 2021, 116550B (5 March 2021); https://doi.org/10.1117/12.2579674