correspondence We first investigated the qualitative performance of each algorithm for images of Alexa Fluor 647-immunolabeled microtubules in fixed COS-7 cells. We recorded data at high imaging density using total internal reflection fluorescence microscopy and direct (d)STORM photoswitching conditions 5 (100 ms integration time, \~4,000 photons fluorophore-1 frame-1). We plotted localizations on raw images, illustrating the characteristic performance of each algorithm (Fig. 1a). SA1 only localized isolated molecules, which were fitted with small localiza-tion error. SA2 localized a larger fraction of the molecules but yielded large localization errors for overlapping molecules. DAOSTORM out-performed both sparse algorithms, identifying almost all molecules with small localization error. We quantified the performance of each algorithm by analyzing simulations of randomly distributed surface-immobilized fluorophores 6. We compared observed localizations to simulated positions, calculating the recall 5 and localization error at different imaging densities. Recall is the percentage of simulated fluorophores detected. Localization error is the root-mean-square distance between a localization and the simulated position. We also measured the precision 5 and redundancy (Supplementary Methods), which did not vary substantially. DAOSTORM substantially outperformed the sparse algorithms in simulations at high signal-to-noise ratio typical of STORM data (bright organic fluorophores, 5,000 photons molecule-1 frame-1 ; Fig. 1b-c). SA1 showed poor recall at high density, with imaging density at half-maximum recall, $\rho$ HM , of 1.2 molecule µm-2. However, SA1 yielded small localization errors even at high imaging density because most overlapping molecules were rejected. SA2 had better recall performance ($\rho$ HM = 3.4 molecules µm-2
:C$\backslash$:/Users/Karoline/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Holden - 2011 - DAOSTORM an algorithm for high- density super-resolution microscopy.pdf:pdf
%0 Generic
%1 Holden2011
%A Holden, Seamus J
%A Uphoff, Stephan
%A Kapanidis, Achillefs N.
%B Nature Methods
%D 2011
%K reconstruction smlm software storm
%N 4
%P 279--280
%R 10.1038/nmeth0411-279
%T DAOSTORM: An algorithm for high-density super-resolution microscopy
%U http://content.ebscohost.com/ContentServer.asp?EbscoContent=dGJyMMvl7ESepq84v\%2BbwOLCmr1GeprVSsa64S7SWxWXS&ContentCustomer=dGJyMOzpsE2xqK5IuePfgeyx9Yvf5ucA&T=P&P=AN&S=R&D=a9h&K=59682926
%V 8
%X correspondence We first investigated the qualitative performance of each algorithm for images of Alexa Fluor 647-immunolabeled microtubules in fixed COS-7 cells. We recorded data at high imaging density using total internal reflection fluorescence microscopy and direct (d)STORM photoswitching conditions 5 (100 ms integration time, \~4,000 photons fluorophore-1 frame-1). We plotted localizations on raw images, illustrating the characteristic performance of each algorithm (Fig. 1a). SA1 only localized isolated molecules, which were fitted with small localiza-tion error. SA2 localized a larger fraction of the molecules but yielded large localization errors for overlapping molecules. DAOSTORM out-performed both sparse algorithms, identifying almost all molecules with small localization error. We quantified the performance of each algorithm by analyzing simulations of randomly distributed surface-immobilized fluorophores 6. We compared observed localizations to simulated positions, calculating the recall 5 and localization error at different imaging densities. Recall is the percentage of simulated fluorophores detected. Localization error is the root-mean-square distance between a localization and the simulated position. We also measured the precision 5 and redundancy (Supplementary Methods), which did not vary substantially. DAOSTORM substantially outperformed the sparse algorithms in simulations at high signal-to-noise ratio typical of STORM data (bright organic fluorophores, 5,000 photons molecule-1 frame-1 ; Fig. 1b-c). SA1 showed poor recall at high density, with imaging density at half-maximum recall, $\rho$ HM , of 1.2 molecule µm-2. However, SA1 yielded small localization errors even at high imaging density because most overlapping molecules were rejected. SA2 had better recall performance ($\rho$ HM = 3.4 molecules µm-2
@misc{Holden2011,
abstract = {correspondence We first investigated the qualitative performance of each algorithm for images of Alexa Fluor 647-immunolabeled microtubules in fixed COS-7 cells. We recorded data at high imaging density using total internal reflection fluorescence microscopy and direct (d)STORM photoswitching conditions 5 (100 ms integration time, {\~{}}4,000 photons fluorophore-1 frame-1). We plotted localizations on raw images, illustrating the characteristic performance of each algorithm (Fig. 1a). SA1 only localized isolated molecules, which were fitted with small localiza-tion error. SA2 localized a larger fraction of the molecules but yielded large localization errors for overlapping molecules. DAOSTORM out-performed both sparse algorithms, identifying almost all molecules with small localization error. We quantified the performance of each algorithm by analyzing simulations of randomly distributed surface-immobilized fluorophores 6. We compared observed localizations to simulated positions, calculating the recall 5 and localization error at different imaging densities. Recall is the percentage of simulated fluorophores detected. Localization error is the root-mean-square distance between a localization and the simulated position. We also measured the precision 5 and redundancy (Supplementary Methods), which did not vary substantially. DAOSTORM substantially outperformed the sparse algorithms in simulations at high signal-to-noise ratio typical of STORM data (bright organic fluorophores, 5,000 photons molecule-1 frame-1 ; Fig. 1b-c). SA1 showed poor recall at high density, with imaging density at half-maximum recall, $\rho$ HM , of 1.2 molecule µm-2. However, SA1 yielded small localization errors even at high imaging density because most overlapping molecules were rejected. SA2 had better recall performance ($\rho$ HM = 3.4 molecules µm-2},
added-at = {2020-03-23T21:12:34.000+0100},
author = {Holden, Seamus J and Uphoff, Stephan and Kapanidis, Achillefs N.},
biburl = {https://www.bibsonomy.org/bibtex/20bd38a0cc77b4a5fa7a8144eeeed4443/kfriedl},
booktitle = {Nature Methods},
doi = {10.1038/nmeth0411-279},
file = {:C$\backslash$:/Users/Karoline/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Holden - 2011 - DAOSTORM an algorithm for high- density super-resolution microscopy.pdf:pdf},
interhash = {b51dfbe88a8ac3e9e53cd37edc2567af},
intrahash = {0bd38a0cc77b4a5fa7a8144eeeed4443},
issn = {15487091},
keywords = {reconstruction smlm software storm},
number = 4,
pages = {279--280},
timestamp = {2020-04-06T15:30:39.000+0200},
title = {{DAOSTORM: An algorithm for high-density super-resolution microscopy}},
url = {http://content.ebscohost.com/ContentServer.asp?EbscoContent=dGJyMMvl7ESepq84v{\%}2BbwOLCmr1GeprVSsa64S7SWxWXS{\&}ContentCustomer=dGJyMOzpsE2xqK5IuePfgeyx9Yvf5ucA{\&}T=P{\&}P=AN{\&}S=R{\&}D=a9h{\&}K=59682926},
volume = 8,
year = 2011
}