Label-free imaging flow cytometry for phenotypic analysis of microalgae populations using deep learning Conference Paper

abstract

• We report a field-portable and high-throughput imaging flow-cytometer to perform label-free phenotypic analysis of microalgae populations by extracting and processing the spatial and spectral features of their reconstructed holographic images using deep learning.

name of conference

• Frontiers in Optics + Laser Science 2021

authors

• Ceylan Koydemir, Hatice

published proceedings

• Frontiers in Optics + Laser Science 2021

author list (cited authors)

• Il, . ., de Haan, K., Grcs, Z., Koydemir, H. C., Peterman, S., Baum, D., ... Ozcan, A.

citation count

• 1

complete list of authors

• Işıl, Çağatay||de Haan, Kevin||Gӧrӧcs, Zoltán||Koydemir, Hatice Ceylan||Peterman, Spencer||Baum, David||Song, Fang||Skandakumar, Thamira||Gumustekin, Esin||Ozcan, Aydogan

publication date

• January 2021

publisher

• Optica Publishing Group  Publisher

keywords

• Biomedical Imaging
• Networking And Information Technology R&d

Digital Object Identifier (DOI)

• 10.1364/fio.2021.fm3d.4

start page

• fm3d.4

URL

• http://dx.doi.org/10.1364/fio.2021.fm3d.4