Kaleido: Visualizing Big Brain Data with Automatic Color Assignment for Single-Neuron Images

Original Article


Effective 3D visualization is essential for connectomics analysis, where the number of neural images easily reaches over tens of thousands. A formidable challenge is to simultaneously visualize a large number of distinguishable single-neuron images, with reasonable processing time and memory for file management and 3D rendering. In the present study, we proposed an algorithm named “Kaleido” that can visualize up to at least ten thousand single neurons from the Drosophila brain using only a fraction of the memory traditionally required, without increasing computing time. Adding more brain neurons increases memory only nominally. Importantly, Kaleido maximizes color contrast between neighboring neurons so that individual neurons can be easily distinguished. Colors can also be assigned to neurons based on biological relevance, such as gene expression, neurotransmitters, and/or development history. For cross-lab examination, the identity of every neuron is retrievable from the displayed image. To demonstrate the effectiveness and tractability of the method, we applied Kaleido to visualize the 10,000 Drosophila brain neurons obtained from the FlyCircuit database (http://www.flycircuit.tw/modules.php?name=kaleido). Thus, Kaleido visualization requires only sensible computer memory for manual examination of big connectomics data.


Neuroimaging Connectome Brain Neuron visualization 



We thank Chao-Chun Chuang, Yen-Jen Lin and Chun-Yen Lin for their assistance in the interface design and software integration. We are grateful to Hsiu-Ming Chang for editorial suggestions. This work was supported by Ministry of Science and Technology grants 105-2112-M-029-002 and 105-2633-B-007-001, and by the Aim for the Top University Project of the Ministry of Education.

Compliance with Ethical Standards

Conflict of Interest

All the authors declare no conflicts of interest.

Supplementary material

12021_2018_9363_MOESM1_ESM.pptx (46 kb)
Figure S1 Benchmark of Kaleido. A-C: Computing resources used versus the number of neurons processed. A: Execution time. B: Peak memory. C: File size of output file. (PPTX 46 kb)
12021_2018_9363_MOESM2_ESM.mp4 (18.2 mb)
Video S1 A 360-degree rotating movie showing the volume rendering result of random 10,000 neurons in the FlyCircuit with Kaleido visualization. (MP4 18591 kb)
12021_2018_9363_MOESM3_ESM.mp4 (25 mb)
Video S2 An orthoslice movie showing the section view of random 10,000 neurons in the FlyCircuit with Kaleido visualization. (MP4 25583 kb)


  1. Ascoli, G. A., Donohue, D. E., & Halavi, M. (2007). NeuroMorpho.Org: a central resource for neuronal morphologies. The Journal of Neuroscience, 27(35), 9247–9251.CrossRefPubMedGoogle Scholar
  2. Binder, K., & Heermann, D. (2010). Monte Carlo simulation in statistical physics: An introduction (5th ed.). New York: Springer.CrossRefGoogle Scholar
  3. Chiang, A. S., et al. (2011). Three-dimensional reconstruction of brain-wide wiring networks in Drosophila at single-cell resolution. Current Biology, 21(1), 1–11.CrossRefPubMedGoogle Scholar
  4. Engel, K., Kraus, M., & Ertl, T. (2001). High-quality pre-integrated volume rendering using hardware-accelerated pixel shading. In Proceedings of the ACM SIGGRAPH/EUROGRAPHICS workshop on graphics hardware (HWWS '01), Hanspeter Pfister (Ed.). ACM, New York, 9–16.Google Scholar
  5. Finger, S. (2001). Origins of neuroscience : a history of explorations into brain function. New York: Oxford University Press.Google Scholar
  6. Goldberg, I. G., et al. (2005). The open microscopy environment (OME) data model and XML file: open tools for informatics and quantitative analysis in biological imaging. Genome Biology, 6(5), R47.CrossRefPubMedPubMedCentralGoogle Scholar
  7. Haehn, D., et al. (2017). Scalable interactive visualization for Connectomics. Informatics, 4(3), 29.  https://doi.org/10.3390/informatics4030029.CrossRefGoogle Scholar
  8. Hampel, S., et al. (2011). Drosophila Brainbow: a recombinase-based fluorescence labeling technique to subdivide neural expression patterns. Nature Methods, 8(3), 253–259.CrossRefPubMedPubMedCentralGoogle Scholar
  9. Hwu, Y., & Margaritondo, G. (2013). Phase contrast: the frontier of x-ray and electron imaging PREFACE. Journal of Physics D-Applied Physics, 46(49).Google Scholar
  10. Landhuis, E. (2017). Neuroscience: Big brain, big data. Nature, 541(7638), 559–561.CrossRefPubMedGoogle Scholar
  11. Lee, T., & Luo, L. (2001). Mosaic analysis with a repressible cell marker (MARCM) for Drosophila neural development. Trends in Neurosciences, 24(5), 251–254.CrossRefPubMedGoogle Scholar
  12. Livet, J., et al. (2007). Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous system. Nature, 450(7166), 56–62.CrossRefPubMedGoogle Scholar
  13. Markram, H., et al. (2015). Reconstruction and simulation of neocortical microcircuitry. Cell, 163, 456–492.CrossRefPubMedGoogle Scholar
  14. Milyaev, N., et al. (2012). The virtual fly brain browser and query interface. Bioinformatics, 28, 411–415.CrossRefPubMedGoogle Scholar
  15. Peng, H., Ruan, Z., Long, F., Simpson, J. H., & Myers, E. W. (2010). V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets. Nature Biotechnology, 28(4), 348–353.CrossRefPubMedPubMedCentralGoogle Scholar
  16. Peng, H., et al. (2015). BigNeuron: large-scale 3D neuron reconstruction from optical microscopy images. Neuron, 87(2), 252–256.CrossRefPubMedPubMedCentralGoogle Scholar
  17. Pettersen, E. F., et al. (2004). UCSF chimera--a visualization system for exploratory research and analysis. Journal of Computational Chemistry, 25(13), 1605–1612.CrossRefPubMedGoogle Scholar
  18. Preucil, F. (1953). Color hue and ink transfer - their relation to perfect reproduction. TAGA Proceedings, pp. 102‑110.Google Scholar
  19. Schindelin, J., Rueden, C. T., Hiner, M. C., & Eliceiri, K. W. (2015). The ImageJ ecosystem: An open platform for biomedical image analysis. Molecular Reproduction and Development, 82(7–8), 518–529.CrossRefPubMedPubMedCentralGoogle Scholar
  20. Schneider, C. A., Rasband, W. S., & Eliceiri, K. W. (2012). NIH image to ImageJ: 25 years of image analysis. Nature Methods, 9(7), 671–675.CrossRefPubMedPubMedCentralGoogle Scholar
  21. Shih, C. T., et al. (2015). Connectomics-based analysis of information flow in the Drosophila brain. Current Biology, 25(10), 1249–1258.CrossRefPubMedGoogle Scholar
  22. Sigal, Y. M., Speer, C. M., Babcock, H. P., & Zhuang, X. W. (2015). Mapping synaptic input fields of neurons with super-resolution imaging. Cell, 163(2), 493–505.CrossRefPubMedPubMedCentralGoogle Scholar
  23. Small, A., & Stahlheber, S. (2014). Fluorophore localization algorithms for super-resolution microscopy (vol 11, pg 267, 2014). Nature Methods, 11(9), 971–971.CrossRefGoogle Scholar
  24. Takemura, S. Y., et al. (2013). A visual motion detection circuit suggested by Drosophila connectomics. Nature, 500, 175–181.CrossRefPubMedPubMedCentralGoogle Scholar
  25. Takemura, S. Y., et al. (2017). A connectome of a learning and memory center in the adult Drosophila brain. eLife, 6, e26975.PubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Biotechnology and Department of Life ScienceNational Tsing Hua UniversityHsinchuTaiwan
  2. 2.National Center for High-performance ComputingHsinchuTaiwan
  3. 3.Department of Computer ScienceNational Chiao Tung UniversityHsinchuTaiwan
  4. 4.Department of Applied PhysicsTunghai UniversityTaichungTaiwan
  5. 5.Brain Research CenterNational Tsing Hua UniversityHsinchuTaiwan
  6. 6.Kavli Institute for Brain and MindUniversity of California at San DiegoLa JollaUSA
  7. 7.Department of Biomedical Science and Environmental BiologyKaohsiung Medical UniversityKaohsiungTaiwan
  8. 8.Institute of Physics, Academia SinicaTaipeiTaiwan

Personalised recommendations