A deep learning-based concept for high throughput image flow cytometry-Reference-Cited by-同舟云学术

A deep learning-based concept for high throughput image flow cytometry

Published:2021-03-22 Issue:12 Volume:118 Page:123701
ISSN:0003-6951
Container-title:Applied Physics Letters
language:en
Short-container-title:Appl. Phys. Lett.

Author:

Martin-Wortham Julie¹²^ORCID,Recktenwald Steffen M.²^ORCID,Lopes Marcelle G. M.²^ORCID,Kaestner Lars²³^ORCID,Wagner Christian²⁴^ORCID,Quint Stephan²^ORCID

Affiliation:

1. Laboratoire Interdisciplinaire de Physique CNRS-UGA, 38402 Saint Martin d'Heres, France

2. Department of Experimental Physics, Saarland University, 66041 Saarbruecken, Germany

3. Theoretical Medicine and Biosciences, Saarland University, 66424 Homburg, Germany

4. Physics and Materials Science Research Unit, University of Luxembourg, L-1511 Luxembourg, Luxembourg

Publisher

AIP Publishing

Subject

Physics and Astronomy (miscellaneous)

Link

https://aip.scitation.org/doi/pdf/10.1063/5.0037336

Reference24 articles.

1. Single-cell microfluidic impedance cytometry: a review

2. Light Microscopy Techniques for Live Cell Imaging

3. High-resolution cytometry of FISH dots in interphase cell nuclei

4. Cameraless high-throughput three-dimensional imaging flow cytometry

5. High-throughput microfluidic imaging flow cytometry

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Lift at low Reynolds number;The European Physical Journal E;2023-11

2. Effect of Cell Age and Membrane Rigidity on Red Blood Cell Shape in Capillary Flow;Cells;2023-06-01

3. Physics-based statistical learning perspectives on droplet formation characteristics in microfluidic cross-junctions;Applied Physics Letters;2022-05-16