Human vs. machine: evaluation of fluorescence micrographs

Nattkemper TW, Twellmann T, Ritter H, Schubert W (2003)
Computers in Biology and Medicine 33(1): 31-43.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
Download
Es wurden keine Dateien hochgeladen. Nur Publikationsnachweis!
Abstract / Bemerkung
To enable high-throughput screening of molecular phenotypes, multi-parameter fluorescence microscopy is applied. Object of our study is lymphocytes which invade human tissue. One important basis for our collaborative project is the development of methods for automatic and accurate evaluation of fluorescence micrographs. As a part of this, we focus on the question of how to measure the accuracy of microscope image interpretation, by human experts or a computer system. Following standard practice we use methods motivated by receiver operator characteristics to discuss the accuracies of human experts and of neural network-based algorithms. For images of good quality the algorithms achieve the accuracy of the medium-skilled experts. In images with increased noise, the classifiers are outperformed by some of the experts. Furthermore, the neural network-based cell detection is much faster than the human experts.
Stichworte
screening (HTS); (ROC); high-throughput; receiver operator characteristics; neural networks; fluorescence microscopy; functional proteomics
Erscheinungsjahr
2003
Zeitschriftentitel
Computers in Biology and Medicine
Band
33
Ausgabe
1
Seite(n)
31-43
ISSN
0010-4825
Page URI
https://pub.uni-bielefeld.de/record/1612741

Zitieren

Nattkemper TW, Twellmann T, Ritter H, Schubert W. Human vs. machine: evaluation of fluorescence micrographs. Computers in Biology and Medicine. 2003;33(1):31-43.
Nattkemper, T. W., Twellmann, T., Ritter, H., & Schubert, W. (2003). Human vs. machine: evaluation of fluorescence micrographs. Computers in Biology and Medicine, 33(1), 31-43. https://doi.org/10.1016/S0010-4825(02)00060-4
Nattkemper, Tim Wilhelm, Twellmann, Thorsten, Ritter, Helge, and Schubert, Walter. 2003. “Human vs. machine: evaluation of fluorescence micrographs”. Computers in Biology and Medicine 33 (1): 31-43.
Nattkemper, T. W., Twellmann, T., Ritter, H., and Schubert, W. (2003). Human vs. machine: evaluation of fluorescence micrographs. Computers in Biology and Medicine 33, 31-43.
Nattkemper, T.W., et al., 2003. Human vs. machine: evaluation of fluorescence micrographs. Computers in Biology and Medicine, 33(1), p 31-43.
T.W. Nattkemper, et al., “Human vs. machine: evaluation of fluorescence micrographs”, Computers in Biology and Medicine, vol. 33, 2003, pp. 31-43.
Nattkemper, T.W., Twellmann, T., Ritter, H., Schubert, W.: Human vs. machine: evaluation of fluorescence micrographs. Computers in Biology and Medicine. 33, 31-43 (2003).
Nattkemper, Tim Wilhelm, Twellmann, Thorsten, Ritter, Helge, and Schubert, Walter. “Human vs. machine: evaluation of fluorescence micrographs”. Computers in Biology and Medicine 33.1 (2003): 31-43.

Link(s) zu Volltext(en)
Access Level
Restricted Closed Access

26 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

Fast and simple tool for the quantification of biofilm-embedded cells sub-populations from fluorescent microscopic images.
Bogachev MI, Volkov VY, Markelov OA, Trizna EY, Baydamshina DR, Melnikov V, Murtazina RR, Zelenikhin PV, Sharafutdinov IS, Kayumov AR., PLoS One 13(5), 2018
PMID: 29715298
Automatic determination of NET (neutrophil extracellular traps) coverage in fluorescent microscopy images.
Coelho LP, Pato C, Friães A, Neumann A, von Köckritz-Blickwede M, Ramirez M, Carriço JA., Bioinformatics 31(14), 2015
PMID: 25792554
Comparison of two automatic cell-counting solutions for fluorescent microscopic images.
Lojk J, Čibej U, Karlaš D, Šajn L, Pavlin M., J Microsc 260(1), 2015
PMID: 26098834
Automated antinuclear immunofluorescence antibody screening: a comparative study of six computer-aided diagnostic systems.
Bizzaro N, Antico A, Platzgummer S, Tonutti E, Bassetti D, Pesente F, Tozzoli R, Tampoia M, Villalta D, Study Group on Autoimmune Diseases of the Italian Society of Laboratory Medicine, Italy., Autoimmun Rev 13(3), 2014
PMID: 24220268
A new era in bioimage informatics.
Murphy RF., Bioinformatics 30(10), 2014
PMID: 24753489
Determining the subcellular location of new proteins from microscope images using local features.
Coelho LP, Kangas JD, Naik AW, Osuna-Highley E, Glory-Afshar E, Fuhrman M, Simha R, Berget PB, Jarvik JW, Murphy RF., Bioinformatics 29(18), 2013
PMID: 23836142
Next-generation biomarkers based on 100-parameter functional super-resolution microscopy TIS.
Schubert W, Gieseler A, Krusche A, Serocka P, Hillert R., N Biotechnol 29(5), 2012
PMID: 22209707
Biological imaging software tools.
Eliceiri KW, Berthold MR, Goldberg IG, Ibáñez L, Manjunath BS, Martone ME, Murphy RF, Peng H, Plant AL, Roysam B, Stuurman N, Swedlow JR, Tomancak P, Carpenter AE., Nat Methods 9(7), 2012
PMID: 22743775
Comparison of parameter-adapted segmentation methods for fluorescence micrographs.
Held C, Palmisano R, Häberle L, Hensel M, Wittenberg T., Cytometry A 79(11), 2011
PMID: 22002887
Spermatogonium image recognition using Zernike moments.
Liyun W, Hefei L, Fuhao Z, Zhengding L, Zhendi W., Comput Methods Programs Biomed 95(1), 2009
PMID: 19268385
A biosegmentation benchmark for evaluation of bioimage analysis methods.
Drelie Gelasca E, Obara B, Fedorov D, Kvilekval K, Manjunath B., BMC Bioinformatics 10(), 2009
PMID: 19878606
Toponomics and neurotoponomics: a new way to medical systems biology.
Schubert W, Bode M, Hillert R, Krusche A, Friedenberger M., Expert Rev Proteomics 5(2), 2008
PMID: 18466063
Automatic segmentation of age-related macular degeneration in retinal fundus images.
Köse C, Sevik U, Gençalioğlu O., Comput Biol Med 38(5), 2008
PMID: 18402931
Functional architecture of the cell nucleus: towards comprehensive toponome reference maps of apoptosis.
Schubert W, Friedenberger M, Bode M, Krusche A, Hillert R., Biochim Biophys Acta 1783(11), 2008
PMID: 18718492
In situ dark field microscopy for on-line monitoring of yeast cultures.
Wei N, You J, Friehs K, Flaschel E, Nattkemper TW., Biotechnol Lett 29(3), 2007
PMID: 17186133
Quantification of vesicles in differentiating human SH-SY5Y neuroblastoma cells by automated image analysis.
Selinummi J, Sarkanen JR, Niemistö A, Linne ML, Ylikomi T, Yli-Harja O, Jalonen TO., Neurosci Lett 396(2), 2006
PMID: 16356645
Automated detection of tunneling nanotubes in 3D images.
Hodneland E, Lundervold A, Gurke S, Tai XC, Rustom A, Gerdes HH., Cytometry A 69(9), 2006
PMID: 16969816
A new preprocessing approach for cell recognition.
Long X, Cleveland WL, Yao YL., IEEE Trans Inf Technol Biomed 9(3), 2005
PMID: 16167695
Multivariate image analysis in biomedicine.
Nattkemper TW., J Biomed Inform 37(5), 2004
PMID: 15488751

35 References

Daten bereitgestellt von Europe PubMed Central.

Real-time molecular and cellular analysis: the new frontier of drug discovery.
Taylor DL, Woo ES, Giuliano KA., Curr. Opin. Biotechnol. 12(1), 2001
PMID: 11167077
The new vision of light microscopy
Taylor, Am. Sci. 90(4), 1992
Image analysis of tissue sections.
Ong SH, Jin XC, Jayasooriah , Sinniah R., Comput. Biol. Med. 26(3), 1996
PMID: 8725778
Image-guided decision support system for pathology
Comaniciu, Mach. Vision Appl. 11(), 1999
Biomedical imaging and the evolution of medical informatics.
Shiffman S, Shortliffe EH., Comput Med Imaging Graph 20(4), 1996
PMID: 8954227
Medical image analysis
Duncan, IEEE Trans. PAMI 22(), 2000

AUTHOR UNKNOWN, 0
Artificial neural network-aided image analysis system for cell counting.
Sjostrom PJ, Frydel BR, Wahlberg LU., Cytometry 36(1), 1999
PMID: 10331623
Knowledge-based analysis of microarray gene expression data using support vector machines
Brown, Proc. Natl. Acad. Sci. 1(), 1997
Computational analysis of microarray data.
Quackenbush J., Nat. Rev. Genet. 2(6), 2001
PMID: 11389458

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0
Self-organized formation of topologically correct feature maps
Kohonen, Biol. Cybernetics 43(), 1982

Kohonen, 1989

AUTHOR UNKNOWN, 0
A neural classifier enabling high-throughput topological analysis of lymphocytes in tissue sections
Nattkemper, IEEE Trans. ITB 5(), 2001

AUTHOR UNKNOWN, 0
Fast training of support vector machines using sequential minimal optimization
Platt, 1998

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0

Cristianini, 2000
ROC methodology in radiologic imaging.
Metz CE., Invest Radiol 21(9), 1986
PMID: 3095258
Signal detectability and medical decision-making.
Lusted LB., Science 171(3977), 1971
PMID: 5545199
Decision-making studies in patient management.
Lusted LB., N. Engl. J. Med. 284(8), 1971
PMID: 4395661
ROC recollected
Lusted, Med. Decision Making 4(), 1984

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0
Leucocyte activation markers in clinical practice.
Viedma Contreras JA., Clin. Chem. Lab. Med. 37(6), 1999
PMID: 10475068
Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Web of Science

Dieser Datensatz im Web of Science®
Quellen

PMID: 12485628
PubMed | Europe PMC

Suchen in

Google Scholar