• DocumentCode
    724955
  • Title

    Neuron crawler: An automatic tracing algorithm for very large neuron images

  • Author

    Zhi Zhou ; Sorensen, Staci A. ; Hanchuan Peng

  • Author_Institution
    Allen Inst. for Brain Sci., Seattle, WA, USA
  • fYear
    2015
  • fDate
    16-19 April 2015
  • Firstpage
    870
  • Lastpage
    874
  • Abstract
    Automatic 3D neuron reconstruction for very large 3D light microscopy images remains to be a challenge in neuroscience. Few existing neuron tracing algorithms can be used with commonly available computers (laptops, desktops, or workstations) to efficiently and accurately reconstruct a neuron in image stacks that are tens of gigabytes or greater. We introduce a new automatic tracing algorithm called Neuron Crawler, which works by first tracing a region of interest (e.g., around the soma), and then iteratively tracing in adjacent image tiles to grow the neuron structure in 3D to its termination point within the image. Our experimental results show that Neuron Crawler can achieve reconstruction accuracy that is comparable to several state-of-the-art algorithms, but with much less computational cost.
  • Keywords
    biomedical optical imaging; cellular biophysics; image reconstruction; iterative methods; medical image processing; neurophysiology; optical microscopy; Neuron Crawler algorithm; automatic 3D neuron reconstruction; automatic neuron tracing algorithm; computational cost; image stack; iterative adjacent image tile tracing; large 3D light microscopy image; large neuron image; neuron structure growing; neuroscience; reconstruction accuracy; region of interest tracing; soma; termination point; Computational efficiency; Computers; Crawlers; Image reconstruction; Memory management; Neurons; Three-dimensional displays; 3D neuron reconstruction; Neuron Crawler; all-path-pruning; large-scale image;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on
  • Conference_Location
    New York, NY
  • Type

    conf

  • DOI
    10.1109/ISBI.2015.7164009
  • Filename
    7164009