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

Abstract

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.

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Fig. 1: Automatic hue angle assignment based on the distance of neurons.
Fig. 2: Visualize the Drosophila connectome with the optimal color assignment, in comparison with random color assignment.
Fig. 3: Patterns revealing various biological meanings hidden behind the images emerge with the differential color-assigning mode.
Fig. 4: Quantitative analysis of Kaleido
Fig. 5: User Interface of Kaleido.

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Acknowledgments

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.

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Correspondence to Nan-Yow Chen or Chi-Tin Shih or Ann-Shyn Chiang.

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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)

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)

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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Wang, TY., Chen, NY., He, GW. et al. Kaleido: Visualizing Big Brain Data with Automatic Color Assignment for Single-Neuron Images. Neuroinform 16, 207–215 (2018). https://doi.org/10.1007/s12021-018-9363-3

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Keywords

  • Neuroimaging
  • Connectome
  • Brain
  • Neuron visualization