CellViCAM-Cell viability classification for animal cell cultures using dark field micrographs

Journal of Biotechnology

Volumn 149, Issue 4, 2010, Pages 310-316

  Burgemeister, S ^a   Nattkemper, T W ^a   Noll, T ^a   Hoffrogge, R ^a   Flaschel, E ^a  

^a BIELEFELD UNIVERSITY   (Germany)

Author keywords

Animal cell cultures; Apoptosis; Cell viability analysis; Dark field microscopy; Image processing; Machine learning

Indexed keywords

APOPTOSIS; AUTOMATED CELL; CELL DENSITY; CELL DETECTION; CELL VIABILITY; CELL VIABILITY ANALYSIS; DARK FIELD; DARK FIELD MICROSCOPY; DATA SETS; DIFFERENTIATED CELLS; FLUORESCENT LABELING; HUMAN CELL LINES; MACHINE-LEARNING; MAMMALIAN CELLS; ONLINE MONITORING; SEMI-AUTOMATICS; SUPERVISED MACHINE LEARNING;

ANIMAL CELL CULTURE; CELL DEATH; CLASSIFICATION (OF INFORMATION); IMAGE PROCESSING; IMAGING SYSTEMS; LEARNING SYSTEMS; MAMMALS; OPTICAL DATA PROCESSING;

CELL CULTURE;

ANIMAL CELL; ANIMAL CELL CULTURE; ARTICLE; CELL DENSITY; CELL VIABILITY; CLASSIFICATION ALGORITHM; COMPUTER PROGRAM; DARK FIELD MICROSCOPY; HUMAN; HUMAN CELL; IMAGE PROCESSING; MICROSCOPY; NONHUMAN; PRIORITY JOURNAL;

APOPTOSIS; ARTIFICIAL INTELLIGENCE; CELL LINE; CELL SURVIVAL; CYTOLOGICAL TECHNIQUES; HUMANS; MICROSCOPY;

ANIMALIA; MAMMALIA;

EID: 77956942873     PISSN: 01681656     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbiotec.2010.07.020     Document Type: Article

References (28)

1. Alexopoulou L., Holt A.C., Medzhitov R., Flavell R.A. Recognition of double-stranded RNA and activation of NF-kappaB by Toll-like receptor 3. Nature 2001, 413:732-738.
2. Boland M., Murphy R. A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells. Bioinformatics 2001, 17:1213-1223.
3. Diamantis A., Magiorkinis E., Sakorafas G.H., Androutsos G. Brief history of apoptosis: from ancient to modern times. Onkologie 2008, 31:702-706.
4. Dickson A.J. Apoptosis regulation and its applications to biotechnology. Trends Biotechnol. 1998, 16:339-342.
5. Duda R.O., Hart P.E. Use of the Hough transformation to detect lines and curves in pictures. Commun. ACM 1972, 15:11-15.
6. Elmore S. Apoptosis: a review of programmed cell death. Toxicol. Pathol. 2007, 35:495-516.
7. Gonzalez R.C., Woods R.E. Digital Image Processing 2001, Prentice Hall, International.
8. Haralick R.M., Dinstein I., Shanmugam K. Textural features for image classification. IEEE Trans. Syst. Man Cybern. 1973, SMC-3:610-621.
9. Heath J.P. Dictionary of Microscopy 2005, John Wiley & Sons, Chinester.
10. Höpfner T., Bluma A., Rudolph G., Lindner P., Scheper T. A review of non-invasive optical based image analysis systems for continuous bioprocess monitoring. Bioprocess Biosyst. Eng. 2010, 33:247-256.
11. Hsu C.W., Lin C.J. A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Netw. 2002, 13:415-425.
12. Joachims T. Making large-scale support vector machine learning practical. Advances in Kernel Methods: Support Vector Learning 1999, 169-184. MIT Press, Cambridge. B. Schölkopf, C.J.C. Burges, A.J. Smola (Eds.).
13. Joeris K., Frerichs J., Konstantinov K., Scheper T. In-situ microscopy: online process monitoring of mammalian cell cultures. Cytotechnology 2002, 38:129-134.
14. Kerr J.F., Wyllie A.H., Currie A.R. Apoptosis: a basic biological phenomenon with wide-ranging implications in tissue kinetics. Br. J. Cancer 1972, 26:239-257.
15. Krysko D.V., Vanden Berghe T., Parthoens E., D'Herde K., Vandenabeele P. Apoptosis and necrosis: detection, discrimination and phagocytosis. Methods 2008, 44:205-221.
16. Maruhashi F., Murakami S., Baba K. Automated monitoring of cell concentration and viability using an image analysis system. Cytotechnology 1994, 15:281-289.
17. Michelson A.A. Studies in Optics 1927, The University of Chicago Press, Chicago.
18. Nattkemper T.W. Automatic segmentation of digital micrographs: a survey. Stud. Health Technol. Inform. 2004, 107:847-851.
19. Northoff, S., Büntemeyer, H., Sandig, V., Zietze, S., Noll, T., 2010. Characterization of the Novel Human AGE1hn Cell Line for Production of Recombinant Proteins. In: Noll, T. (Ed.), Cells and Culture. Springer Netherlands, Dordrecht, pp. 693-697.
20. Pratt W. Digital Image Processing 1991, John Wiley & Sons, New York.
21. Rodenacker K., Bengtsson E. A feature set for cytometry on digitized microscopic images. Anal. Cell. Pathol. 2002, 25:1-36.
22. Rose T., Winkler K., Brundke E., Jordan I., Sandig V. Alternative strategies and new cell lines for high-level production of biopharmaceuticals. Modern Biopharmaceuticals 2005, 761-777. Wiley-VCH, Weinheim. J. Knäblein (Ed.).
23. Rudolph G., Lindner P., Gierse A., Bluma A., Martinez G., Hitzmann B., Scheper T. Online monitoring of microcarrier based fibroblast cultivations with in situ microscopy. Biotechnol. Bioeng. 2008, 99:136-145.
24. Vapnik V.N. The Nature of Statistical Learning Theory 1995, Springer-Verlag, New York.
25. Walsh G. Biopharmaceuticals: recent approvals and likely directions. Trends Biotechnol. 2005, 23:553-558.
26. Wei N., You J., Friehs K., Flaschel E., Nattkemper T.W. An in situ probe for on-line monitoring of cell density and viability on the basis of dark field microscopy in conjunction with image processing and supervised machine learning. Biotechnol. Bioeng. 2007, 97:1489-1500.
27. Wei N., You J., Friehs K., Flaschel E., Nattkemper T.W. In situ dark field microscopy for on-line monitoring of yeast cultures. Biotechnol. Lett. 2006, 29:373-378.
28. Zeng A.P. Cell culture kinetics and modeling. Cell Culture Technology for Pharmaceutical and Cell-Based Therapies 2006, 299-348. CRC Press, London. S.S. Ozturk, W.-S. Hu (Eds.).