Real-time Image Processing for Microscopy-based Label-free Imaging Flow Cytometry in a Microfluidic Chip

Scientific Reports

Volumn 7, Issue 1, 2017, Pages

  Heo, Young Jin ^a   Lee, Donghyeon ^a   Kang, Junsu ^a   Lee, Keondo ^a   Chung, Wan Kyun ^a  

^a Pohang University of Science and Technology (POSTECH)   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85029505529     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/s41598-017-11534-0     Document Type: Article

References (24)

1. Robinson, J. P., Roederer, M. Flow cytometry strikes gold. Science 350, 739-740 (2015).
2. Han, Y., Gu, Y., Zhang, A. C., Lo, Y. H. Review: Imaging technologies for flow cytometry. Lab Chip 16, 4639-4647 (2016).
3. Henery, S., et al. Quantitative image based apoptotic index measurement using multispectral imaging flow cytometry: A comparison with standard photometric methods. Apoptosis 13, 1054 (2008).
4. Ouk, C., JayatVignoles, C., Donnard, M., Feuillard, J. Both CD62 and CD162 antibodies prevent formation of CD36dependent platelets, rosettes, and artefactual pseudoexpression of platelet markers on white blood cells: A study with ImageStream®. Cytom. Part A 79, 477-484 (2011).
5. Liu, Z., et al. Circulating tumor cell detection in hepatocellular carcinoma based on karyoplasmic ratios using imaging flow cytometry. Sci. Rep. 6, 39808, doi: Https://doi.org/10.1038/srep39808 (2016).
6. Vorobjev, I. A., , Barteneva, N. S. Quantitative Functional Morphology by Imaging Flow Cytometry in Imaging Flow Cytometry: Methods and Protocols 3-11 (Springer, 2016).
7. Piyasena, M. E., Graves, S. W. The intersection of flow cytometry with microfluidics and microfabrication. Lab Chip 14, 1044-1059 (2014).
8. Goda, K., et al. High-throughput single-microparticle imaging flow analyzer. Proc. Natl. Acad. Sci. USA 109, 11630-11635 (2012).
9. Diebold, E. D., Buckley, B. W., Gossett, D. R., Jalali, B. Digitally synthesized beat frequency multiplexing for sub-millisecond fluorescence microscopy. Nat. Photonics 7, 806-810 (2013).
10. Perlman, Z. E., et al. Multidimensional drug profiling by automated microscopy. Science 306, 1194-1198 (2004).
11. Blasi, T., et al. Label-free cell cycle analysis for high-throughput imaging flow cytometry. Nat. Commun. 7, 10256, doi: Https://doi.org/10.1038/ncomms10256 (2016).
12. Carpenter, A. E., et al. CellProfiler: Image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100, doi: Https://doi.org/10.1186/gb-2006-7-10-r100 (2006).
13. Chen, C. L., et al. Deep learning in label-free cell classification. Sci. Rep. 6, 21471, doi: Https://doi.org/10.1038/srep21471 (2016).
14. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P. Gradient-based learning applied to document recognition. P. IEEE 86, 2278-2324 (1998).
15. Xie, W., Noble, J. A., Zisserman, A. Microscopy cell counting and detection with fully convolutional regression networks. Comput. Method. Biomec.: Imaging, Visualization 1-10 (2016).
16. Xie, Y., Xing, F., Kong, X., Su, H., Yang, L. Beyond classification: Structured regression for robust cell detection using convolutional neural network in International Conference on Medical Image Computing and Computer-Assisted Intervention 358-365 (Springer International Publishing, 2015).
17. Habibzadeh, M., Krzyżak, A., Fevens, T. White blood cell differential counts using convolutional neural networks for low resolution images in International Conference on Artificial Intelligence and Soft Computing 263-274 (Springer Berlin Heidelberg, 2013).
18. Maaten, L. V. D., Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579-2605 (2008).
19. LeCun, Y., Bengio, Y., Hinton, G. Deep learning. Nature 521(7553), 436-444 (2015).
20. Ren, S., He, K., Girshick, R., Sun, J. Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems. 91-99 (2015).
21. Redmon, J., Divvala, S., Girshick, R., Farhadi, A. You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 779-778 (2016).
22. Liu, W., et al. SSD: Single shot multibox detector. European Conference on Computer Vision. 21-37 (Springer International Publishing, 2016).
23. Cheezum, M. K., Walker, W. F., Guilford, W. H. Quantitative comparison of algorithms for tracking single fluorescent particles. Biophys. J. 81, 2378-2388 (2001).
24. Xia, Y., Whitesides, G. M. Soft lithography. Annu. Rev. Mater. Sci. 28, 153-184 (1998).