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Using neural networks for high-speed blood cell classification in a holographic-microscopy flow-cytometry system

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Center for nano- and biophotonics (NB-Photonics)
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.
Keywords
neural network, Digital inline holography, flow cytometry, cell sorting, integrated optics

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Citation

Please use this url to cite or link to this publication:

MLA
Schneider, Bendix et al. “Using Neural Networks for High-speed Blood Cell Classification in a Holographic-microscopy Flow-cytometry System.” Proceedings of SPIE. Vol. 9328. BELLINGHAM: SPIE-INT SOC OPTICAL ENGINEERING, 2015. Print.
APA
Schneider, B., Vanmeerbeeck, G., Stahl, R., Lagae, L., & Bienstman, P. (2015). Using neural networks for high-speed blood cell classification in a holographic-microscopy flow-cytometry system. Proceedings of SPIE (Vol. 9328). Presented at the Conference on Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XIII, BELLINGHAM: SPIE-INT SOC OPTICAL ENGINEERING.
Chicago author-date
Schneider, Bendix, G Vanmeerbeeck, R Stahl, L Lagae, and Peter Bienstman. 2015. “Using Neural Networks for High-speed Blood Cell Classification in a Holographic-microscopy Flow-cytometry System.” In Proceedings of SPIE. Vol. 9328. BELLINGHAM: SPIE-INT SOC OPTICAL ENGINEERING.
Chicago author-date (all authors)
Schneider, Bendix, G Vanmeerbeeck, R Stahl, L Lagae, and Peter Bienstman. 2015. “Using Neural Networks for High-speed Blood Cell Classification in a Holographic-microscopy Flow-cytometry System.” In Proceedings of SPIE. Vol. 9328. BELLINGHAM: SPIE-INT SOC OPTICAL ENGINEERING.
Vancouver
1.
Schneider B, Vanmeerbeeck G, Stahl R, Lagae L, Bienstman P. Using neural networks for high-speed blood cell classification in a holographic-microscopy flow-cytometry system. Proceedings of SPIE. BELLINGHAM: SPIE-INT SOC OPTICAL ENGINEERING; 2015.
IEEE
[1]
B. Schneider, G. Vanmeerbeeck, R. Stahl, L. Lagae, and P. Bienstman, “Using neural networks for high-speed blood cell classification in a holographic-microscopy flow-cytometry system,” in Proceedings of SPIE, San Francisco, CA, 2015, vol. 9328.
@inproceedings{7182495,
  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.},
  articleno    = {93281F},
  author       = {Schneider, Bendix and Vanmeerbeeck, G and Stahl, R and Lagae, L and Bienstman, Peter},
  booktitle    = {Proceedings of SPIE},
  isbn         = {978-1-62841-418-9},
  issn         = {0277-786X},
  keywords     = {neural network,Digital inline holography,flow cytometry,cell sorting,integrated optics},
  language     = {eng},
  location     = {San Francisco, CA},
  pages        = {4},
  publisher    = {SPIE-INT SOC OPTICAL ENGINEERING},
  title        = {Using neural networks for high-speed blood cell classification in a holographic-microscopy flow-cytometry system},
  url          = {http://dx.doi.org/10.1117/12.2079436},
  volume       = {9328},
  year         = {2015},
}

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