Super-resolution microscopy has become essential for the study of nanoscale biological processes. This type of imaging often requires the use of specialised image analysis tools to process a large volume of recorded data and extract quantitative information. In recent years, our team has built an open-source image analysis framework for super-resolution microscopy designed to combine high performance and ease of use. We named it NanoJ - a reference to the popular ImageJ software it was developed for. In this paper, we highlight the current capabilities of NanoJ for several essential processing steps: spatio-temporal alignment of raw data (NanoJ-Core), super-resolution image reconstruction (NanoJ-SRRF), image quality assessment (NanoJ-SQUIRREL), structural modelling (NanoJ-VirusMapper) and control of the sample environment (NanoJ-Fluidics). We expect to expand NanoJ in the future through the development of new tools designed to improve quantitative data analysis and measure the reliability of fluorescent microscopy studies.
%0 Journal Article
%1 Laine2018
%A Laine, Romain F
%A Tosheva, Kalina L
%A Gustafsson, Nils
%A Gray, Robert D M
%A Almada, Pedro
%A Albrecht, David
%A Risa, Gabriel T
%A Hurtig, Fredrik
%A Lind\aas, Ann-christin
%A Baum, Buzz
%A Mercer, Jason
%A Leterrier, Christophe
%A Pereira, Pedro M
%A Culley, Siân
%A Henriques, Ricardo
%D 2018
%J bioRxiv
%K imagej quantitative software tracing
%N October
%P 432674
%R 10.1101/432674
%T NanoJ: a high-performance open-source super-resolution microscopy toolbox
%X Super-resolution microscopy has become essential for the study of nanoscale biological processes. This type of imaging often requires the use of specialised image analysis tools to process a large volume of recorded data and extract quantitative information. In recent years, our team has built an open-source image analysis framework for super-resolution microscopy designed to combine high performance and ease of use. We named it NanoJ - a reference to the popular ImageJ software it was developed for. In this paper, we highlight the current capabilities of NanoJ for several essential processing steps: spatio-temporal alignment of raw data (NanoJ-Core), super-resolution image reconstruction (NanoJ-SRRF), image quality assessment (NanoJ-SQUIRREL), structural modelling (NanoJ-VirusMapper) and control of the sample environment (NanoJ-Fluidics). We expect to expand NanoJ in the future through the development of new tools designed to improve quantitative data analysis and measure the reliability of fluorescent microscopy studies.
@article{Laine2018,
abstract = {Super-resolution microscopy has become essential for the study of nanoscale biological processes. This type of imaging often requires the use of specialised image analysis tools to process a large volume of recorded data and extract quantitative information. In recent years, our team has built an open-source image analysis framework for super-resolution microscopy designed to combine high performance and ease of use. We named it NanoJ - a reference to the popular ImageJ software it was developed for. In this paper, we highlight the current capabilities of NanoJ for several essential processing steps: spatio-temporal alignment of raw data (NanoJ-Core), super-resolution image reconstruction (NanoJ-SRRF), image quality assessment (NanoJ-SQUIRREL), structural modelling (NanoJ-VirusMapper) and control of the sample environment (NanoJ-Fluidics). We expect to expand NanoJ in the future through the development of new tools designed to improve quantitative data analysis and measure the reliability of fluorescent microscopy studies.},
added-at = {2020-03-23T21:12:34.000+0100},
author = {Laine, Romain F and Tosheva, Kalina L and Gustafsson, Nils and Gray, Robert D M and Almada, Pedro and Albrecht, David and Risa, Gabriel T and Hurtig, Fredrik and Lind{\aa}s, Ann-christin and Baum, Buzz and Mercer, Jason and Leterrier, Christophe and Pereira, Pedro M and Culley, Si{\^{a}}n and Henriques, Ricardo},
biburl = {https://www.bibsonomy.org/bibtex/2a1f00a1567b3b489e812f6aae7728325/kfriedl},
doi = {10.1101/432674},
file = {:C$\backslash$:/Users/Karoline/Documents/Abbelight/Literatur/432674.full.pdf:pdf},
interhash = {c2208ae1d167aee228301a2de6494859},
intrahash = {a1f00a1567b3b489e812f6aae7728325},
journal = {bioRxiv},
keywords = {imagej quantitative software tracing},
number = {October},
pages = 432674,
timestamp = {2020-03-23T21:58:07.000+0100},
title = {{NanoJ: a high-performance open-source super-resolution microscopy toolbox}},
year = 2018
}