Design and Implementation for Massively Parallel Automated Localization of Neurons for Brain Circuits

  • Dan ZouEmail author
  • Hong Ye
  • Min Zhu
  • Xiaoqian Zhu
  • Liangyuan Zhou
  • Fei Xia
  • Lina Lu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 553)


Automatic localization of neurons is the foundation of tracing and reconstructing the neuronal connections from the brain image stacks. With rapid development of fluorescence labeling and imaging at submicron resolution, a huge amount of data were generated, making it challenging to efficiently locate neurons from massive multidimensional images. In this manuscript, we present the implementation of an efficient parallel neuronal localization algorithm that is based on NeuroGPS. We split the image stack with a space overlapping scheme to eliminate the communication overhead among computing nodes. On this basis, we develop a hybrid parallel automated neuronal localization algorithm. We evaluate this implementation on the TianHe-2 supercomputer. The preliminary results on a terabyte-sized image stack indicate that it is capable of handle large data sets and obtains good scalability and computing performance.


Brain imaging Neuronal localization Parallel algorithm 



This research is partially supported by the Chinese 973 Program (2015CB755604) and the National Natural Science Foundation of China (61502516, 61572515). We greatly thank Hang Zhou for his constructive comments and useful discussions on details of NeuroGPS.


  1. 1.
    Lu J. Neuronal Tracing for Connectomic Studies. Neuroinformatics, 9, 159–166 (2011).Google Scholar
  2. 2.
    Lichtman J.W., Denk W. The Big and the Small: Challenges of Imaging the Brain’s Circuits. Science, 334, 618–623 (2011).Google Scholar
  3. 3.
    Alivisatos, A.P., Chun, M., Church, G.M., et al. The brain activity map project and the challenge of functional connectomics. Neuron, 74(6), 970–974 (2012).Google Scholar
  4. 4.
    Briggman K.L., Bock D.D. Volume electron microscopy for neuronal circuit reconstruction. Current opinion in neurobiology, 22, 154–161 (2011).Google Scholar
  5. 5.
    Long F., Peng H., Liu X., et al. A 3d digital atlas of C. elegans and its application to single-cell analyses. Nature Methods, 6, 667–672 (2009).Google Scholar
  6. 6.
    Wahlby C., Sintorn I. M., et al. Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections. Journal of Microscopy, 215, 67–76 (2004).Google Scholar
  7. 7.
    Bashar M. K., Komatsu K., Fujimori T., et al. Automatic Extraction of Nuclei Centroids of Mouse Embryonic Cells from Fluorescence Microscopy Images. PLOS ONE, 7 (2012).Google Scholar
  8. 8.
    Al-Kofahi Y., Lassoued W., et al. Improved automatic detection and segmentation of cell nuclei in histopathology images. IEEE transactions on bio-medical engineering, 57, 841–852 (2010).Google Scholar
  9. 9.
    Wienert S., Heim D., Saeger K., et al. Detection and segmentation of cell nuclei in virtual microscopy images: a minimum-model approach. Scientific Reports, 2, 00503 (2012).Google Scholar
  10. 10.
    Li G., Liu T., et al. 3D cell nuclei segmentation based on gradient flow tracking. BMC Cell Biology, 8, 40 (2007).Google Scholar
  11. 11.
    Liu T., Li G., et al. An automated method for cell detection in zebrafish. Neuroinformatics, 6, 5–21 (2008).Google Scholar
  12. 12.
    Quan T., Zheng T., Yang Z., et al. NeuroGPS: automated localization of neurons for brain circuits using L1 minimization model. Scientific Reports, 3, 1414 (2013).Google Scholar
  13. 13.
    Yuan J., Gong H., Li A., et al. Visible rodent brain-wide networks at single-neuron resolution. Frontiers in neuroanatomy, 9, 70 (2015).Google Scholar
  14. 14.
    A. LaTorre, L. Alonso-Nanclares, et al. 3D segmentations of neuronal nuclei from confocal microscope image stacks. Frontiers in neuroanatomy, 7, 1–10 (2013).Google Scholar
  15. 15.
    D. A. Vousden, J. Epp, et al., Whole-brainmapping of behaviourally induced neural activation in mice. Brain Structure and Function, 220(4), 2043–2057 (2015).Google Scholar
  16. 16.
    Gong H., Zeng S., Yan C., et al. Continuously tracing brain-wide long-distance axonal projections in mice at a one-micron voxel resolution. NeuroImage, 87–98 (2013).Google Scholar
  17. 17.
    Ragan T., Kadiri L. R., Venkataraju K. U., et al. Serial two-photon tomography for automated ex vivo mouse brain imaging. Nature Methods, 9, 255–258 (2012).Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Dan Zou
    • 1
    Email author
  • Hong Ye
    • 2
  • Min Zhu
    • 1
  • Xiaoqian Zhu
    • 1
  • Liangyuan Zhou
    • 1
  • Fei Xia
    • 3
  • Lina Lu
    • 4
  1. 1.Academy of Ocean Science and EngineeringNational University of Defense TechnologyChangshaChina
  2. 2.Network CenterBeijing Technology and Business UniversityBeijingChina
  3. 3.Electronic Engineering CollegeNaval University of EngineeringWuhanChina
  4. 4.College of Mechatronic Engineering and AutomationNational University of Defense TechnologyChangshaChina

Personalised recommendations