Reference : A deep learning-based concept for high throughput image flow cytometry
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Physics
A deep learning-based concept for high throughput image flow cytometry
Martin-Wortham, Julie [Quint, S (Corresponding Author), Saarland Univ, Dept Expt Phys, D-66041 Saarbrucken, Germany. Martin-Wortham, Julie, Lab Interdisciplinaire Phys CNRS UGA, F-38402 St Martin Dheres, France. Martin-Wortham, Julie]
Recktenwald, Steffen M. [Recktenwald, Steffen M.]
Lopes, Marcelle G. M. [Lopes, Marcelle G. M.]
Kaestner, Lars [Kaestner, Lars]
Wagner, Christian mailto [University of Luxembourg > Department of Physics and Material Sciences]
Quint, Stephan [Quint, Stephan, Saarland Univ, Dept Expt Phys, D-66041 Saarbrucken, Germany. Kaestner, Lars, Saarland Univ, Theoret Med Biosci, D-66424 Homburg, Germany. Wagner, Christian, Univ Luxembourg, Phys Mat Sci Res Unit, L-1511 Luxembourg, Luxembourg.]
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[en] We propose a flow cytometry concept that combines a spatial optical modulation scheme and deep learning for lensless cell imaging. Inspired by auto-encoder techniques, an artificial neural network mimics the optical transfer function of a particular microscope and camera for certain types of cells once trained and reconstructs microscope images from simple waveforms that are generated by cells in microfluidic flow. This eventually enables the label-free detection of cells at high throughput while simultaneously providing their corresponding brightfield images. The present work focuses on the computational proof of concept of this method by mimicking the waveforms. Our suggested approach would require a minimum set of optical components such as a collimated light source, a slit mask, and a light sensor and could be easily integrated into a ruggedized lab-on-chip device. The method is benchmarked with a well-investigated dataset of red blood cell images.

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