, Volume 16, Issue 2, pp 207–215 | Cite as

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

  • Ting-Yuan Wang
  • Nan-Yow Chen
  • Guan-Wei He
  • Guo-Tzau Wang
  • Chi-Tin Shih
  • Ann-Shyn Chiang
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 ( 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)


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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

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