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