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Proceedings Paper

Using neural networks for high-speed blood cell classification in a holographic-microscopy flow-cytometry system
Author(s): B. Schneider; G. Vanmeerbeeck; R. Stahl; L. Lagae; P. Bienstman
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Paper Abstract

High-throughput cell sorting with flow cytometers is an important tool in modern clinical cell studies. Most cytometers use biomarkers that selectively bind to the cell, but induce significant changes in morphology and inner cell processes leading sometimes to its death. This makes label-based cell sorting schemes unsuitable for further investigation. We propose a label-free technique that uses a digital inline holographic microscopy for cell imaging and an integrated, optical neural network for high-speed classification. The perspective of dense integration makes it attractive to ultrafast, large-scale cell sorting. Network simulations for a ternary classification task (monocytes/granulocytes/lymphocytes) resulted in 89% accuracy.

Paper Details

Date Published: 2 March 2015
PDF: 4 pages
Proc. SPIE 9328, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XIII, 93281F (2 March 2015); doi: 10.1117/12.2079436
Show Author Affiliations
B. Schneider, Univ. Gent (Belgium)
G. Vanmeerbeeck, IMEC (Belgium)
R. Stahl, IMEC (Belgium)
L. Lagae, IMEC (Belgium)
KU Leuven (Belgium)
P. Bienstman, Univ. Gent (Belgium)


Published in SPIE Proceedings Vol. 9328:
Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XIII
Daniel L. Farkas; Dan V. Nicolau; Robert C. Leif, Editor(s)

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