Title |
CellProfiler: Novel Automated Image Segmentation Procedure for Super-Resolution Microscopy
|
---|---|
Published in |
Biological Procedures Online, August 2015
|
DOI | 10.1186/s12575-015-0023-9 |
Pubmed ID | |
Authors |
Kareem Soliman |
Abstract |
Super resolution (SR) microscopy enabled cell biologists to visualize subcellular details up to 20 nm in resolution. This breakthrough in spatial resolution made image analysis a challenging procedure. Direct and automated segmentation of SR images remains largely unsolved, especially when it comes to providing meaningful biological interpretations. Here, we introduce a novel automated imaging analysis routine, based on Gaussian, followed by a segmentation procedure using CellProfiler software (www.cellprofiler.org). We tested this method and succeeded to segment individual nuclear pore complexes stained with gp210 and pan-FG proteins and captured by two-color STED microscopy. Test results confirmed accuracy and robustness of the method even in noisy STED images of gp210. Our pipeline and novel segmentation procedure may benefit end-users of SR microscopy to analyze their images and extract biologically significant quantitative data about them in user-friendly and fully-automated settings. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Chile | 1 | 17% |
United Kingdom | 1 | 17% |
Unknown | 4 | 67% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 5 | 83% |
Practitioners (doctors, other healthcare professionals) | 1 | 17% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 2 | 5% |
Switzerland | 2 | 5% |
Germany | 1 | 2% |
Netherlands | 1 | 2% |
Unknown | 38 | 86% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 11 | 25% |
Researcher | 8 | 18% |
Student > Bachelor | 6 | 14% |
Student > Postgraduate | 4 | 9% |
Student > Master | 4 | 9% |
Other | 3 | 7% |
Unknown | 8 | 18% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 13 | 30% |
Biochemistry, Genetics and Molecular Biology | 7 | 16% |
Physics and Astronomy | 4 | 9% |
Chemistry | 3 | 7% |
Engineering | 2 | 5% |
Other | 7 | 16% |
Unknown | 8 | 18% |