| Title |
Visualization and Analysis of 3D Microscopic Images
|
|---|---|
| Published in |
PLoS Computational Biology, June 2012
|
| DOI | 10.1371/journal.pcbi.1002519 |
| Pubmed ID | |
| Authors |
Fuhui Long, Jianlong Zhou, Hanchuan Peng |
| Abstract |
In a wide range of biological studies, it is highly desirable to visualize and analyze three-dimensional (3D) microscopic images. In this primer, we first introduce several major methods for visualizing typical 3D images and related multi-scale, multi-time-point, multi-color data sets. Then, we discuss three key categories of image analysis tasks, namely segmentation, registration, and annotation. We demonstrate how to pipeline these visualization and analysis modules using examples of profiling the single-cell gene-expression of C. elegans and constructing a map of stereotyped neurite tracts in a fruit fly brain. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 17% |
| United Kingdom | 3 | 17% |
| Curaçao | 1 | 6% |
| Switzerland | 1 | 6% |
| Canada | 1 | 6% |
| Czechia | 1 | 6% |
| France | 1 | 6% |
| Colombia | 1 | 6% |
| Unknown | 6 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 8 | 44% |
| Members of the public | 6 | 33% |
| Science communicators (journalists, bloggers, editors) | 2 | 11% |
| Practitioners (doctors, other healthcare professionals) | 1 | 6% |
| Unknown | 1 | 6% |
Mendeley readers
The data shown below were compiled from readership statistics for 239 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 5 | 2% |
| United States | 3 | 1% |
| France | 2 | <1% |
| United Kingdom | 2 | <1% |
| Japan | 2 | <1% |
| Ukraine | 1 | <1% |
| Colombia | 1 | <1% |
| Portugal | 1 | <1% |
| Spain | 1 | <1% |
| Other | 0 | 0% |
| Unknown | 221 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 70 | 29% |
| Student > Ph. D. Student | 53 | 22% |
| Student > Master | 25 | 10% |
| Professor > Associate Professor | 18 | 8% |
| Student > Bachelor | 12 | 5% |
| Other | 31 | 13% |
| Unknown | 30 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 78 | 33% |
| Computer Science | 26 | 11% |
| Engineering | 25 | 10% |
| Biochemistry, Genetics and Molecular Biology | 22 | 9% |
| Neuroscience | 16 | 7% |
| Other | 35 | 15% |
| Unknown | 37 | 15% |