Neuroinformatics

, Volume 15, Issue 2, pp 133–149 | Cite as

SparseTracer: the Reconstruction of Discontinuous Neuronal Morphology in Noisy Images

Original Article

Abstract

Digital reconstruction of a single neuron occupies an important position in computational neuroscience. Although many novel methods have been proposed, recent advances in molecular labeling and imaging systems allow for the production of large and complicated neuronal datasets, which pose many challenges for neuron reconstruction, especially when discontinuous neuronal morphology appears in a strong noise environment. Here, we develop a new pipeline to address this challenge. Our pipeline is based on two methods, one is the region-to-region connection (RRC) method for detecting the initial part of a neurite, which can effectively gather local cues, i.e., avoid the whole image analysis, and thus boosts the efficacy of computation; the other is constrained principal curves method for completing the neurite reconstruction, which uses the past reconstruction information of a neurite for current reconstruction and thus can be suitable for tracing discontinuous neurites. We investigate the reconstruction performances of our pipeline and some of the best state-of-the-art algorithms on the experimental datasets, indicating the superiority of our method in reconstructing sparsely distributed neurons with discontinuous neuronal morphologies in noisy environment. We show the strong ability of our pipeline in dealing with the large-scale image dataset. We validate the effectiveness in dealing with various kinds of image stacks including those from the DIADEM challenge and BigNeuron project.

Keywords

Digital reconstruction Automatic tracing Neuronal morphology Constrained principal curves 

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Shiwei Li
    • 1
    • 2
  • Hang Zhou
    • 1
    • 2
  • Tingwei Quan
    • 1
    • 2
    • 3
  • Jing Li
    • 1
    • 2
  • Yuxin Li
    • 1
    • 2
  • Anan Li
    • 1
    • 2
  • Qingming Luo
    • 1
    • 2
  • Hui Gong
    • 1
    • 2
  • Shaoqun Zeng
    • 1
    • 2
  1. 1.Britton Chance Center for Biomedical PhotonicsHuazhong University of Science and Technology-Wuhan National Laboratory for OptoelectronicsWuhanChina
  2. 2.MoE Key Laboratory for Biomedical Photonics, Department of Biomedical EngineeringHuazhong University of Science and TechnologyWuhanChina
  3. 3.School of Mathematics and StatisticsHubei University of EducationWuhanChina

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