Dictionary-enhanced imaging cytometry

Scientific Reports

Volumn 7, Issue , 2017, Pages

  Orth, Antony ^a,b   Schaak, Diane ^a   Schonbrun, Ethan ^a  

^a HARVARD UNIVERSITY   (United States)

^b RMIT UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

BLOOD CELL; HUMAN; IMAGE CYTOMETRY; IMAGE PROCESSING; MICROSCOPY; PROCEDURES;

BLOOD CELLS; HUMANS; IMAGE CYTOMETRY; IMAGE PROCESSING, COMPUTER-ASSISTED; MICROSCOPY;

EID: 85013647280     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep43148     Document Type: Article

References (35)

1. Huang, K. & Murphy, R. F. From Quantitative Microscopy to Automated Image Understanding. J. Biomed. Opt. 9, 893-912 (2004).
2. Shariff, A., Kangas, J., Coelho, L. P., Quinn, S. & Murphy, R. F. Automated image analysis for high-content screening and analysis. J. Biomol. Screen. 15, 726-734 (2010).
3. Pepperkok, R. & Ellenberg, J. High-throughput fluorescence microscopy for systems biology. Nat. Rev. Mol. Cell Biol. 7, 690-696 (2006).
4. Orth, A. & Crozier, K. Microscopy with microlens arrays: high throughput, high resolution and light-field imaging. Opt Express 20, 13522-13531 (2012).
5. Orth, A. & Crozier, K. Gigapixel fluorescence microscopy with a water immersion microlens array. Opt. Express 21, 2361-2368 (2013).
6. Orth, A. & Crozier, K. B. High throughput multichannel fluorescence microscopy with microlens arrays. Opt. Express 22, 18101-18112 (2014).
7. Orth, A., Tomaszewski, M. J., Ghosh, R. N. & Schonbrun, E. Gigapixel multispectral microscopy. Optica 2, 654-662 (2015).
8. Isikman, S. O., Greenbaum, A., Luo, W., Coskun, A. F. & Ozcan, A. Giga-pixel lensfree holographic microscopy and tomography using color image sensors. PloS One 7, e45044 (2012).
9. Zheng, G., Ou, X. & Yang, C. 0.5 gigapixel microscopy using a flatbed scanner. Biomed. Opt. Express 5, 1-8 (2014).
10. Arpali, S. A., Arpali, C., Coskun, A. F., Chiang, H.-H. & Ozcan, A. High-throughput screening of large volumes of whole blood using structured illumination and fluorescent on-chip imaging. Lab. Chip 12, 4968-4971 (2012).
11. Bickle, M. The beautiful cell: high-content screening in drug discovery. Anal. Bioanal. Chem. 398, 219-226 (2010).
12. Zanella, F., Lorens, J. B. & Link, W. High content screening: seeing is believing. Trends Biotechnol. 28, 237-245 (2010).
13. Theera-Umpon, N. & Dhompongsa, S. Morphological granulometric features of nucleus in automatic bone marrow white blood cell classification. IEEE Trans. Inf. Technol. Biomed. Publ. IEEE Eng. Med. Biol. Soc. 11, 353-359 (2007).
14. Hiremath, P. S., Bannigidad, P. & Geeta, S. Automated identification and classification of white blood cells (leukocytes) in digital microscopic images. IJCA Spec. Issue recent Trends Image Process. Pattern Recognition RTIPPR 59-63 (2010).
15. Rohde, G. K., Ribeiro, A. J. S., Dahl, K. N. & Murphy, R. F. Deformation-based nuclear morphometry: Capturing nuclear shape variation in HeLa cells. Cytometry A 73A, 341-350 (2008).
16. Krsns, A., Larsen, R. & Dahl, A. Learning histopathological patterns. J. Pathol. Inform. 2, 12 (2011).
17. Yang, L. et al. Parallel content-based sub-image retrieval using hierarchical searching. Bioinformatics btt623, doi: 10.1093/ bioinformatics/btt623 (2013).
18. Freeman, W. T., Jones, T. R. & Pasztor, E. C. Example-based super-resolution. Comput. Graph. Appl. IEEE 22, 56-65 (2002).
19. Elad, M. & Aharon, M. Image denoising via sparse and redundant representations over learned dictionaries. Image Process. IEEE Trans. On 15, 3736-3745 (2006).
20. Marwah, K., Wetzstein, G., Bando, Y. & Raskar, R. Compressive light field photography using overcomplete dictionaries and optimized projections. ACM Trans. Graph. TOG 32, 46 (2013).
21. Diego, F., Reichinnek, S., Both, M. & Hamprecht, F. A. Automated identification of neuronal activity from calcium imaging by sparse dictionary learning. In 2013 IEEE 10th International Symposium on Biomedical Imaging 1058-1061 (IEEE, 2013).
22. Hu, T. et al. Electron microscopy reconstruction of brain structure using sparse representations over learned dictionaries. IEEE Trans. Med. Imaging 32, 2179-2188 (2013).
23. Naeim, F., Rao, P. N. & Grody, W. W. Hematopathology: Morphology, Immunophenotype, Cytogenetics, and Molecular Approaches. (Academic Press, 2009).
24. Ortolani, C. Flow Cytometry of Hematological Malignancies. (John Wiley & Sons, 2011).
25. McLachlan, G. Discriminant Analysis and Statistical Pattern Recognition. (John Wiley & Sons, 2004).
26. Wong, K. L. et al. The three human monocyte subsets: implications for health and disease. Immunol. Res. 53, 41-57 (2012).
27. Orth, A. Classification of white blood cell nuclei http://gigapan.com/gigapans/194956 (2016).
28. Kolaczkowska, E. & Kubes, P. Neutrophil recruitment and function in health and inflammation. Nat. Rev. Immunol. 13, 159-175 (2013).
29. McClatchey, K. D. Clinical laboratory medicine. (Lippincott Williams & Wilkins, 2002).
30. Mortensen, K. I., Churchman, L. S., Spudich, J. A. & Flyvbjerg, H. Optimized localization analysis for single-molecule tracking and super-resolution microscopy. Nat. Methods 7, 377-381 (2010).
31. Boland, M. V. & Murphy, R. F. A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells. Bioinformatics 17, 1213-1223 (2001).
32. Murphy, R. F. Putting proteins on the map. Nat. Biotechnol. 24, 1223-1224 (2006).
33. George, T. C. et al. Distinguishing modes of cell death using the ImageStream multispectral imaging flow cytometer. Cytometry A 59, 237-245 (2004).
34. Orth, A. 260k white blood cell nuclei http://www.gigapan.com/gigapans/189594 (2016).
35. Orth, A. WBC smear http://www.gigapan.com/gigapans/189654 (2016).