Label-free analysis of micro-algae populations using a high-throughput holographic imaging flow cytometer and deep learning

Cagatay Isil; Kevin De Haan; Hatice Ceylan Koydemir; Zoltán Göröcs; David Baum; Fang Song; Thamira Skandakumar; Esin Gumustekin; Aydogan Ozcan

doi:10.1117/12.2579674

5 March 2021 Label-free analysis of micro-algae populations using a high-throughput holographic imaging flow cytometer and deep learning

Cagatay Isil, Kevin De Haan, Hatice Ceylan Koydemir, Zoltán Göröcs, David Baum, Fang Song, Thamira Skandakumar, Esin Gumustekin, Aydogan Ozcan

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Proceedings Volume 11655, Label-free Biomedical Imaging and Sensing (LBIS) 2021; 116550B (2021) https://doi.org/10.1117/12.2579674
Event: SPIE BiOS, 2021, Online Only

Abstract

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, and 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

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KEYWORDS

Holography

3D image reconstruction

Holograms

Statistical analysis

Microfluidics

Ocean optics

Organisms

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