Detection of neuron membranes in electron microscopy images using a serial neural network architecture

Elizabeth Jurrus, Antonio R.C. Paiva, Shigeki Watanabe, James R. Anderson, Bryan W. Jones, Ross T. Whitaker, Erik M. Jorgensen, Robert E. Marc, Tolga Tasdizen

Research output: Contribution to journalArticlepeer-review

72 Scopus citations


Study of nervous systems via the connectome, the map of connectivities of all neurons in that system, is a challenging problem in neuroscience. Towards this goal, neurobiologists are acquiring large electron microscopy datasets. However, the shear volume of these datasets renders manual analysis infeasible. Hence, automated image analysis methods are required for reconstructing the connectome from these very large image collections. Segmentation of neurons in these images, an essential step of the reconstruction pipeline, is challenging because of noise, anisotropic shapes and brightness, and the presence of confounding structures. The method described in this paper uses a series of artificial neural networks (ANNs) in a framework combined with a feature vector that is composed of image intensities sampled over a stencil neighborhood. Several ANNs are applied in series allowing each ANN to use the classification context provided by the previous network to improve detection accuracy. We develop the method of serial ANNs and show that the learned context does improve detection over traditional ANNs. We also demonstrate advantages over previous membrane detection methods. The results are a significant step towards an automated system for the reconstruction of the connectome.

Original languageEnglish (US)
Pages (from-to)770-783
Number of pages14
JournalMedical image analysis
Issue number6
StatePublished - Dec 2010
Externally publishedYes


  • Artificial neural networks
  • Contour completion
  • Machine learning
  • Membrane detection
  • Neural circuit reconstruction

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design


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