Skip to main content
SpringerLink
Log in

Reconstruction, Techniques and Validation

  • Reference work entry
  • First Online:
Encyclopedia of Computational Neuroscience

  • 89 Accesses

Definition

Reconstruction algorithms are used to build a geometric model of neurons, describing their morphology, from two-dimensional and three-dimensional images. Validation methods are used to determine the accuracy of the resulting models. Both reconstruction and validation are active areas of research, where proposed algorithms must address several complex problems, including (a) the use of a wide range of imaging modalities used to collect data, (b) the complex topological structure of interconnected branches within the neurons, and (c) automation for large data sets. Several methods have been proposed for addressing these issues; however, current reconstruction algorithms can be broadly placed into four categories: semiautomated software packages, local exploration, global processing, and crowdsourcing-based approaches.

Detailed Description

Neuronal reconstructions provide geometric representations of cell and network morphology that can be used to perform quantitative analysis...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 2,499.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Ascoli GA, Krichmar JL, Nasuto SJ, Senft SL (2001) Generation, description and storage of dendritic morphology data. Philos Trans R Soc Lond B 356(1412):1131–1145

    CAS  Google Scholar 

  • Bower JM, Beeman D (1998) The book of GENESIS: exploring realistic neural models with the GEneral NEural SImulation System. Springer, New York

    Google Scholar 

  • Carnevale NT, Hines ML (2009) The neuron book. Cambridge University Press, Cambridge

    Google Scholar 

  • Cuntz H, Forstner F, Borst A, Häusser M (2011) The TREES toolbox-probing the basis of axonal and dendritic branching. Neuroinformatics 9:91–96

    PubMed  Google Scholar 

  • Eberhard J, Wanner A, Wittum G (2006) NeuGen: a tool for the generation of realistic morphology of cortical neurons and neural networks in 3D. Neurocomputing 70(3):327–342

    Google Scholar 

  • Fiala JC (2005) Reconstruct: a free editor for serial section microscopy. J Microsc 218:52–61

    CAS  PubMed  Google Scholar 

  • Giuly RJ, Kim K-Y, Ellisman MH (2013) Dp2: distributed 3D image segmentation using micro-labor workforce. Bioinformatics 29(10):1359–1360

    PubMed Central  CAS  PubMed  Google Scholar 

  • Gleeson P, Steuber V, Silver RA (2007) neuroConstruct: a tool for modeling networks of neurons in 3D space. Neuron 54(2):219–235

    PubMed Central  CAS  PubMed  Google Scholar 

  • Halavi M, Hamilton KA, Parekh R, Ascoli GA (2012) Digital reconstructions of neuronal morphology: three decades of research trends. Front Neurosci 6:49

    PubMed Central  PubMed  Google Scholar 

  • Helmstaedter M, Briggman KL, Denk W (2011) High-accuracy neurite reconstruction for high-throughput neuroanatomy. Nat Neurosci 14:1081–1088

    CAS  PubMed  Google Scholar 

  • Jain V, Bollmann B, Richardson M, Berger D, Helmstaedter M, Briggman K, Denk W, Bowden J, Mendenhall J, Abraham W, Harris K, Kasthuri N, Hayworth K, Schalek R, Tapia JC, Lichtman J, Seung HS (2010a). Boundary learning by optimization with topological constraints. In: IEEE conference on computer vision and pattern recognition, San Francisco, CA, pp 2488–2495

    Google Scholar 

  • Jain V, Seung SH, Turaga SC (2010b) Machines that learn to segment images: a crucial technology for connectomics. Curr Opin Neurobiol 20:1–14

    Google Scholar 

  • Liu Y (2011) The DIADEM and beyond. Neuroinformatics 9:99–102

    PubMed  Google Scholar 

  • Luebke D, Reddy M, Cohen J, Varshney A, Watson B, Huebner R (2002) Level of detail for 3D graphics. Morgan Kaufmann, Palo Alto

    Google Scholar 

  • Luisi J, Narayanaswamy A, Galbreath Z, Roysam B (2011) The FARSIGHT trace editor: an open source tool for 3-d inspection and efficient pattern analysis aided editing of automated neuronal reconstructions. Neuroinformatics 9:305–315

    PubMed  Google Scholar 

  • Mayerich D, Bjornsson C, Taylor J, Roysam B (2012) NetMets: software for quantifying and visualizing errors in biological network segmentation. BMC Bioinformatics 13(Suppl 8):S7

    PubMed Central  PubMed  Google Scholar 

  • Meijering E (2010) Neuron tracing in perspective. Cytometry A 77A:693–704

    Google Scholar 

  • Meila M (2003) Comparing clusterings by the variation of information. Learning theory and Kernel machines. Lect Notes Comput Sci 2777:173–187

    Google Scholar 

  • Mishchenko Y, Hu T, Spacek J, Mendenhall J, Harris KM, Chklovskii DB (2010) Ultrastructural analysis of hippocampal neuropil from the connectomics perspective. Neuron 67:1009–1020

    PubMed Central  CAS  PubMed  Google Scholar 

  • Seung S, Burnes L (2012) Eyewire. http://eyewire.org/

Download references

Author information

Authors and Affiliations

  1. Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, 405 N. Matthews Ave, Urbana, IL, 61801, USA

    David Mayerich

  2. Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA

    Yoonsuck Choe & John Keyser

Authors
  1. David Mayerich
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yoonsuck Choe
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. John Keyser
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David Mayerich .

Editor information

Editors and Affiliations

  1. Department of Biology, Emory University, Atlanta, GA, USA

    Dieter Jaeger

  2. Department of Biomedical Engineering, Florida International University, Miami, FL, USA

    Ranu Jung

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media New York

About this entry

Cite this entry

Mayerich, D., Choe, Y., Keyser, J. (2015). Reconstruction, Techniques and Validation. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6675-8_288

Download citation

Publish with us

Policies and ethics

Search

Navigation