| Title |
Kaleido: Visualizing Big Brain Data with Automatic Color Assignment for Single-Neuron Images
|
|---|---|
| Published in |
Neuroinformatics, March 2018
|
| DOI | 10.1007/s12021-018-9363-3 |
| Pubmed ID | |
| Authors |
Ting-Yuan Wang, Nan-Yow Chen, Guan-Wei He, Guo-Tzau Wang, Chi-Tin Shih, Ann-Shyn Chiang |
| 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. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 3 | 60% |
| France | 1 | 20% |
| Unknown | 1 | 20% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 60% |
| Scientists | 2 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 10 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Lecturer | 2 | 20% |
| Student > Ph. D. Student | 2 | 20% |
| Student > Bachelor | 1 | 10% |
| Researcher | 1 | 10% |
| Professor > Associate Professor | 1 | 10% |
| Other | 0 | 0% |
| Unknown | 3 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 4 | 40% |
| Biochemistry, Genetics and Molecular Biology | 1 | 10% |
| Agricultural and Biological Sciences | 1 | 10% |
| Medicine and Dentistry | 1 | 10% |
| Unknown | 3 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 May 2018.
All research outputs
#13,740,804
of 24,319,828 outputs
Outputs from Neuroinformatics
#205
of 421 outputs
Outputs of similar age
#162,033
of 335,728 outputs
Outputs of similar age from Neuroinformatics
#9
of 16 outputs
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So far Altmetric has tracked 421 research outputs from this source. They receive a mean Attention Score of 4.5. This one has gotten more attention than average, scoring higher than 51% of its peers.
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We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.