| Title |
Soma Detection in 3D Images of Neurons using Machine Learning Technique
|
|---|---|
| Published in |
Neuroinformatics, October 2017
|
| DOI | 10.1007/s12021-017-9342-0 |
| Pubmed ID | |
| Authors |
Guan-Wei He, Ting-Yuan Wang, Ann-Shyn Chiang, Yu-Tai Ching |
| Abstract |
Computing and analyzing the neuronal structure is essential to studying connectome. Two important tasks for such analysis are finding the soma and constructing the neuronal structure. Finding the soma is considered more important because it is required for some neuron tracing algorithms. We describe a robust automatic soma detection method developed based on the machine learning technique. Images of neurons were three-dimensional confocal microscopic images in the FlyCircuit database. The testing data were randomly selected raw images that contained noises and partial neuronal structures. The number of somas in the images was not known in advance. Our method tries to identify all the somas in the images. Experimental results showed that the method is efficient and robust. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 33% |
| Austria | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 19 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 37% |
| Researcher | 3 | 16% |
| Student > Master | 3 | 16% |
| Student > Bachelor | 1 | 5% |
| Professor > Associate Professor | 1 | 5% |
| Other | 1 | 5% |
| Unknown | 3 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 3 | 16% |
| Neuroscience | 3 | 16% |
| Mathematics | 2 | 11% |
| Agricultural and Biological Sciences | 2 | 11% |
| Computer Science | 2 | 11% |
| Other | 3 | 16% |
| Unknown | 4 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 30 October 2017.
All research outputs
#14,304,466
of 23,007,053 outputs
Outputs from Neuroinformatics
#214
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Outputs of similar age
#179,764
of 326,301 outputs
Outputs of similar age from Neuroinformatics
#3
of 6 outputs
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