Human vs. machine: Evaluation of fluorescence micrographs

Computers in Biology and Medicine

Volumn 33, Issue 1, 2003, Pages 31-43

  Nattkemper, Tim W ^a   Twellmann, Thorsten ^a   Ritter, Helge ^a   Schubert, Walter ^b,c  

^a BIELEFELD UNIVERSITY   (Germany)

^b OTTO VON GUERICKE UNIVERSITY   (Germany)

^c MelTec   (Germany)

Author keywords

Fluorescence microscopy; Functional proteomics; High throughput screening (HTS); Neural networks; Receiver operator characteristics (ROC)

Indexed keywords

COMPUTER SYSTEMS; FLUORESCENCE; NEURAL NETWORKS; TISSUE;

MICROGRAPHS;

MEDICINE;

ACCURACY; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOANALYSIS; COMPUTER SYSTEM; FLUORESCENCE MICROSCOPY; HUMAN; HUMAN TISSUE; IMAGE ANALYSIS; IMAGE QUALITY; LYMPHOCYTE; MAN MACHINE INTERACTION; NOISE; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; SKILL; STANDARD; COMPARATIVE STUDY; DATA BASE; IMAGE PROCESSING; PATHOLOGY; SENSITIVITY AND SPECIFICITY;

DIAGNOSTIC AGENT; MEMBRANE ANTIGEN; MONOCLONAL ANTIBODY;

ALGORITHMS; ANTIBODIES, MONOCLONAL; ANTIGENS, SURFACE; DATABASES; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; LYMPHOCYTES; MICROSCOPY, FLUORESCENCE; NEURAL NETWORKS (COMPUTER); SENSITIVITY AND SPECIFICITY;

EID: 0037212267     PISSN: 00104825     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0010-4825(02)00060-4     Document Type: Article

References (35)

1. Taylor D.L., Woo E.S., Giuliano K.A. Real-time molecular and cellular analysis. the new frontier of drug discovery Curr. Opinion Biotech. 12:2001;75-81.
2. Taylor D.L., Nederlof M., Lanni F., Waggoner A.S. The new vision of light microscopy. Am. Sci. 90(4):1992;322-335.
3. Ong S.H., Jin X.C., Jayasooriah R.S. Image analysis of tissue sections. Comput. Biol. Med. 26:1996;269-279.
4. Comaniciu D., Meer P., Foran D.J. Image-guided decision support system for pathology. Mach. Vision Appl. 11:1999;213-224.
5. Shiffman S., Shortliffe E.H. Biomedical imaging and the evolution of medical informatics. Comput. Med. Imag. Graph. 20(4):1996;189-192.
6. Duncan J.S., Ayache N. Medical image analysis. progress over two decades and the challenges ahead IEEE Trans. PAMI. 22:2000;85-105.
7. Schubert W. Antigenic determinants of. t -lymphozyte αβ receptor and other leucocyte surface proteins as differential markers of skeletal muscle regeneration detection of spatially and timely restricted patterns by MAM microscopy Eur. J. Cell Biol. 58:1992;395-410.
8. W. Schubert, Molecular semiotic structures in the cellular immune system: key to dynamics and spatial patterning? in: W.Z.J. Parisi, S.C. Mueller (Eds.), A Perspective Look at Nonlinear Media in Physics, Chemistry and Biology, Lecture Notes in Physics, Springer, Berlin, 1997.
9. Sjöstrom P., Frydel B., Wahlberg L. Artificial neural network-aided image analysis system for cell counting. Cytometry. 36:1999;18-26.
10. 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. 17:2001;1213-1223.
11. Brown M.P.S., Grundy W.N., Lin D., Cristianini N., Sugnet C., Furey T.S., Ares M.J., Haussler D. Knowledge-based analysis of microarray gene expression data using support vector machines. Proc. Natl. Acad. Sci. 1:1997;262-267.
12. Quackenbush J. Computational analysis of microarray data. Nat. Rev. Genetics. 2:2001;418-427.
13. B. Moghaddam, M.-H. Yang, Sex with support vector machines, in: T.K. Leen, T.G. Dietterich, V. Tresp (Eds.), Advances in Neural Information Processing Systems, Vol. 13, MIT Press, Cambridge, MA, 2001, pp. 960-966.
14. H. Ritter, Learning with the self-organizing map, in: T. Kohonen (Ed.), Artificial Neural Networks, Vol. 1, Elsevier, Amsterdam, 1991.
15. Kohonen T. Self-organized formation of topologically correct feature maps. Biol. Cybernetics. 43:1982;59-69.
16. Kohonen T. Self-Organization and Associative Memory. 1989;Springer, Berlin.
17. D. Rumelhart, G. Hinton, R. Williams, Learning internal representations by error propagation, in: D.E. Rumelhart, J.L. McClelland (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition 1: Foundatioris, 1986, pp. 318-362.
18. Nattkemper T.W., Ritter H., Schubert W. A neural classifier enabling high-throughput topological analysis of lymphocytes in tissue sections. IEEE Trans. ITB. 5:2001;138-149.
19. T. Kämpfe, T.W. Nattkemper, H. Ritter, Combining independent component analysis and self-organizing maps for cell image classification, Proc. DAGM Lecture Notes in Computer Science no. 2191 (2001) 262-268.
20. Bell T., Sejnowski T. An information-maximization approach to blind source separation and blind deconvolution. Neural Comput. 6:1995;1129-1159.
21. Platt J. Fast training of support vector machines using sequential minimal optimization. Schölkopf B., Burges C., Smola A. Advances in Kernel Methods - Support Vector Learning. 1998;MIT press, Cambridge, MA.
22. S.S. Keerthi, S.K. Shevade, C. Bhattacharyya, K.R.K. Murthy, Improvements to Platt's SMO algorithm for SVM classifier design, Technical Report TR CD-99-14, National University of Singapore, 1999.
23. T. Twellmann, T.W. Nattkemper, W. Schubert, H. Ritter, Cell detection in micrographs of tissue sections using support vector machines, in: Proceedings of the ICANN: workshop on Kernel & Subspace Methods for Computer Vision, Vienna, Austria, 2001, pp. 79-88.
24. A.J. Smola, B. Schölkopf, A tutorial on support vector regression, Neurocolt Technical Report nc-tr-98-030, Royal Holloway College, University of London, UK, 1998.
25. C.J.C. Burges, A tutorial on support vector machines for pattern recognition, Knowledge Discovery Data Min. 2 (1998), pp. 121-167.
26. Cristianini N., Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. 2000;Cambridge University Press, Cambridge.
27. Metz C. ROC methodology in radiologic imaging. Invest. Radiol. 21:1986;720-733.
28. Lusted L. Signal detectability and medical decision making. Science. 171:1971;1217-1219.
29. Lusted L. Decision making studies in patient management. N. Engl. J. Med. 284:1984;416-424.
30. Lusted L. ROC recollected. Med. Decision Making. 4:1984;131-135.
31. Zweig H.M., Campbell G. Receiver-operating characteristic (ROC) plots. a fundamental evaluation tool in clinical medicine Clin. Chem. 39:1993;561-577.
32. Y.Y.Y. Shimosato, K. Yamagishi, Experience and present status of telepathology in the national cancer center hospital, Zentralbl. Pathol. 138 (1992) 413-417.
33. Ackerman A. Discordance among expert pathologists in diagnosis of melanocytic neoplasms. Hum. Pathol. 27:1996;1115-1116.
34. F.J.W.-M. Leong, A.K. Graham, T. Gahm, J.O. McGee, Telepathology: clinical utility and methodology, in: D.G. Lowe, J.C.E. Underwood (Eds.), Recent Advances in Histopathology, Vol. 18, Churchill Livingstone, London, 1999.
35. Viedma Contreras J. Leucocyte activation markers in clinical practice. Clin. Chem. Lab. Med. 37(6):1999;607-622 Tim Wilhelm Nattkemper studied mathematics, physical chemistry and computer science at the University of Bielefeld, Germany. After post-graduate work for the Neuroinformatics Group and the Department of Macro-Economic Dynamics at the University of Bielefeld, he received a Ph.D. in Computer Science at the university of Bielefeld in 2001. He is a member of the Neuroinformatics Group, the graduate program "Structure Formation Phenomena" and the "International Graduate School in Bioinformatics and Genome Research". Since 2002 he is head of the newly founded group "Applied Neuroinformatis" at the Bielefeld University, Faculty of Technology.