Multilayer Neural Networks with Receptive Fields as a Model for the Neuron Reconstruction Problem

  • Wojciech Czarnecki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7268)


The developed model consists of a multilayer neural network with receptive fields used to estimate the local direction of the neuron on a fragment of microscopy image. It can be used in a wide range of classical neuron reconstruction methods (manual, semi-automatic, local automatic or global automatic), some of which are also outlined in this paper. The model is trained on an automatically generated training set extracted from a provided example image stack and corresponding reconstruction file. During the experiments the model was tested in simple statistical tests and in real applications, and achieved good results. The main advantage of the proposed approach is its simplicity for the end-user, one who might have little or no mathematical/computer science background, as it does not require any manual configuration of constants.


artificial neural network receptive fields computer vision neuron reconstruction 


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  1. 1.
    Roysam, B., Shain, W., Ascoli, G.A.: The central role of neuroinformatics in the national academy of engineering’s grandest challenge: reverse engineer the brain. Neuroinformatics 7, 1–5 (2009)CrossRefGoogle Scholar
  2. 2.
    Ascoli, G.A.: Computational Neuroanatomy: Principles and Methods. Humana Press, New Jersey (2002)CrossRefGoogle Scholar
  3. 3.
    Capowski, J.J.: An automatic neuron reconstruction system. Journal of Neuroscience Methods 8, 353–364 (1983)CrossRefGoogle Scholar
  4. 4.
    Al-Kofahi, K.A., Lasek, S., Szarowski, D.H., Pace, C.J., Nagy, G., Turner, J.N., Roysam, B.: Rapid automated three-dimensional tracing of neurons from confocal image stacks. IEEE Trans. Informat. Technol. Biomed. 6, 171–187 (2002)CrossRefGoogle Scholar
  5. 5.
    Peng, H., Ruan, Z., Atasoy, D., Sternson, S.: Automatic reconstruction of 3D neuron structures using a graph-augmented deformable model. Bioinformatics 26, 138–146 (2010)Google Scholar
  6. 6.
    Evers, J.F., Schmitt, S., Sibila, M., Duch, C.: Progress in functional neuroanatomy: Precise automatic geometric reconstruction of neuronal morphology from confocal image stacks. Journal of Neurophysiology 93, 2331–2342 (2005)CrossRefGoogle Scholar
  7. 7.
    Peng, H., Ruan, Z., Long, F., Simpson, J.H., Myers, E.W.: V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets. Nature Biotechnology 28(4), 348–353 (2010)CrossRefGoogle Scholar
  8. 8.
    Burge, C.B.: Modeling dependencies in pre-mRNA splicing signals. Computational Methods in Molecular Biology, 127–163 (1998)Google Scholar
  9. 9.
    Jones, D.T.: Protein secondary structure prediction based on position-specific scoring matrices. Journal of Molecular Biology 292(2), 195–202 (1999)CrossRefGoogle Scholar
  10. 10.
    Brown, M.P.S., Grundy, W.N., Lin, D., Cristianini, N., Sugnet, C.W., Furey, T.S., Ares, M., Haussler, D.: Knowledge-based analysis of microarray gene expression data by using support vector machines. Proceedings of the National Academy of Sciences of the United States of America 97(1), 262–267 (2000)CrossRefGoogle Scholar
  11. 11.
    Turaga, S.C., Murray, J.F., Jain, V., Roth, F., Helmstaedter, M., Briggman, K., Denk, W., Seung, H.S.: Convolutional networks can learn to generate affinity graphs for image segmentation. Neural Computation 22(2), 511–538 (2010)zbMATHCrossRefGoogle Scholar
  12. 12.
    Kreshuk, A., Straehle, C.N., Sommer, C., Koethe, U., Cantoni, M., Knott, G., Hamprecht, F.A.: Automated detection and segmentation of synaptic contacts in nearly isotropic serial electron microscopy images. PLoS ONE 6 (2011)Google Scholar
  13. 13.
    Phung, S.L., Bouzerdoum, A.: A pyramidal neural network for visual pattern recognition. IEEE Transactions on Neural Networks 18(2), 329–343 (2007)CrossRefGoogle Scholar
  14. 14.
    Haykin, S.: Neural Networks and Learning Machines. Prentice Hall (2008)Google Scholar
  15. 15.
    Egmont-Petersen, M.: Image processing with neural networks - a review. Pattern Recognition 35, 2279–2301 (2002)zbMATHCrossRefGoogle Scholar
  16. 16.
    Sarle, W.S.: Stopped Training and Other Remedies for Overfitting. In: Proceedings of the 27th Symposium on the Interface of Computing Science and Statistics, pp. 352–360 (1995)Google Scholar
  17. 17.
    Brown, K.M., Barrionuevo, G., Canty, A.J., De Paola, V., Hirsch, J.A., Jefferis, G.S.X.E., Lu, J., Snippe, M., Sugihara, I., Ascoli, G.A.: The diadem data sets: Representative light microscopy images of neuronal morphology to advance automation of digital reconstructions. Neuroinformatics 9(2-3), 143–157 (2011)CrossRefGoogle Scholar
  18. 18.
    Gillette, T., Brown, K., Ascoli, G.A.: The diadem metric: Comparing multiple reconstructions of the same neuron. Neuroinformatics 9, 233–245 (2011)CrossRefGoogle Scholar
  19. 19.
    Zhao, T., Xie, J., Amat, F., Clack, N., Ahammad, P., Peng, H., Long, F., et al.: Automated Reconstruction of Neuronal Morphology Based on Local Geometrical and Global Structural Models. Neuroinformatics 9(2-3), 247–261 (2011)CrossRefGoogle Scholar
  20. 20.
    Fernandes, B.J.T., Cavalcanti, G.D.C., Ren, T.I.: Nonclassical receptive field inhibition applied to image segmentation. Neural Network World 19, 21–36 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Wojciech Czarnecki
    • 1
  1. 1.Department of Computer Linguistics and Artificial Intelligence, Faculty of Mathematics and Computer ScienceAdam Mickiewicz UniversityPoznanPoland

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