Title |
The fidelity of stochastic single-molecule super-resolution reconstructions critically depends upon robust background estimation
|
---|---|
Published in |
Scientific Reports, January 2014
|
DOI | 10.1038/srep03854 |
Pubmed ID | |
Authors |
Eelco Hoogendoorn, Kevin C. Crosby, Daniela Leyton-Puig, Ronald M. P. Breedijk, Kees Jalink, Theodorus W. J. Gadella, Marten Postma |
Abstract |
The quality of super resolution images obtained by stochastic single-molecule microscopy critically depends on image analysis algorithms. We find that the choice of background estimator is often the most important determinant of reconstruction quality. A variety of techniques have found use, but many have a very narrow range of applicability depending upon the characteristics of the raw data. Importantly, we observe that when using otherwise accurate algorithms, unaccounted background components can give rise to biases on scales defeating the purpose of super-resolution microscopy. We find that a temporal median filter in particular provides a simple yet effective solution to the problem of background estimation, which we demonstrate over a range of imaging modalities and different reconstruction methods. |
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Singapore | 1 | 11% |
Netherlands | 1 | 11% |
France | 1 | 11% |
United States | 1 | 11% |
Unknown | 2 | 22% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 5 | 56% |
Scientists | 4 | 44% |
Mendeley readers
Geographical breakdown
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Netherlands | 5 | 3% |
Germany | 3 | 2% |
France | 3 | 2% |
United States | 3 | 2% |
United Kingdom | 2 | 1% |
Czechia | 1 | <1% |
Argentina | 1 | <1% |
Chile | 1 | <1% |
Unknown | 133 | 88% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 45 | 30% |
Student > Ph. D. Student | 37 | 24% |
Student > Master | 12 | 8% |
Student > Bachelor | 10 | 7% |
Student > Doctoral Student | 8 | 5% |
Other | 20 | 13% |
Unknown | 20 | 13% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 39 | 26% |
Physics and Astronomy | 31 | 20% |
Biochemistry, Genetics and Molecular Biology | 21 | 14% |
Engineering | 12 | 8% |
Chemistry | 8 | 5% |
Other | 19 | 13% |
Unknown | 22 | 14% |