%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 }