Neuroinformatics

, Volume 11, Issue 1, pp 5–29

Semi-Automated Neuron Boundary Detection and Nonbranching Process Segmentation in Electron Microscopy Images

  • Elizabeth Jurrus
  • Shigeki Watanabe
  • Richard J. Giuly
  • Antonio R. C. Paiva
  • Mark H. Ellisman
  • Erik M. Jorgensen
  • Tolga Tasdizen
Original Article

DOI: 10.1007/s12021-012-9149-y

Cite this article as:
Jurrus, E., Watanabe, S., Giuly, R.J. et al. Neuroinform (2013) 11: 5. doi:10.1007/s12021-012-9149-y

Abstract

Neuroscientists are developing new imaging techniques and generating large volumes of data in an effort to understand the complex structure of the nervous system. The complexity and size of this data makes human interpretation a labor-intensive task. To aid in the analysis, new segmentation techniques for identifying neurons in these feature rich datasets are required. This paper presents a method for neuron boundary detection and nonbranching process segmentation in electron microscopy images and visualizing them in three dimensions. It combines both automated segmentation techniques with a graphical user interface for correction of mistakes in the automated process. The automated process first uses machine learning and image processing techniques to identify neuron membranes that deliniate the cells in each two-dimensional section. To segment nonbranching processes, the cell regions in each two-dimensional section are connected in 3D using correlation of regions between sections. The combination of this method with a graphical user interface specially designed for this purpose, enables users to quickly segment cellular processes in large volumes.

Keywords

Machine learning Membrane detection Artificial neural networks Filter bank Contour completion Neural circuit reconstruction Connectomics 

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Elizabeth Jurrus
    • 1
    • 2
  • Shigeki Watanabe
    • 3
  • Richard J. Giuly
    • 4
  • Antonio R. C. Paiva
    • 1
  • Mark H. Ellisman
    • 4
  • Erik M. Jorgensen
    • 3
  • Tolga Tasdizen
    • 1
    • 5
  1. 1.Scientific Computing and Imaging InstituteUniversity of UtahSalt Lake CityUSA
  2. 2.School of ComputingUniversity of UtahSalt Lake CityUSA
  3. 3.Department of BiologyUniversity of UtahSalt Lake CityUSA
  4. 4.National Center for Microscopy and Imaging ResearchUniversity of CaliforniaSan DiegoUSA
  5. 5.Department of Electrical EngineeringUniversity of UtahSalt Lake CityUSA

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