Cutting-edge super-resolution image analysis in napari through NanoJ
Type: Applications - napari Plugin Foundations grants
Principal Investigator: Ricardo Henriques
Investigators: Bruno Saraiva, Ricardo Henriques
Start-date: January 2023
End-date: December 2023
Website: https://www.napari-hub.org/plugins/napari-nanopyx
NanoJ is an instrumental ImageJ-based computational framework that provides several analytical tasks to enhance or enable super-resolution microscopy. It also hosts popular GPU-based algorithms, including NanoJ-SRRF, which provides the capacity to super-resolve images from conventional microscopes without needing super-resolution specific hardware; and NanoJ-SQUIRREL, which quantitatively detects artifacts created by super-resolution methods. While the use of NanoJ by the scientific community continues to increase, the aged libraries it depends on constrains its development.
This project will refactor the ImageJ NanoJ plugin framework, including SRRF, into an ecosystem of napari plugins to enable next-generation high-performance, super-resolution microscopy analysis.
Technology explored
Supported publications
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Rxiv-Maker: an automated template engine for streamlined scientific publications Bruno M. Saraiva, Guillaume Jacquemet, Ricardo Henriques Preprint published in Zenodo, July 2025 Technologies: Rxiv-Maker () Funded by: CZI, EMBO, ERC, H2021 and H2022 DOI: 10.5281/zenodo.15805470 |
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Efficiently accelerated bioimage analysis with NanoPyx, a Liquid Engine-powered Python framework Bruno M. Saraiva, Inês Cunha, António D. Brito, Gautier Follain, Raquel Portela, Robert Haase, Pedro M. Pereira, Guillaume Jacquemet, Ricardo Henriques Paper published in Nature Methods, January 2025 Technologies: Fast4DReg (), NanoJ (), NanoJ-eSRRF (), NanoJ-SQUIRREL () and NanoPyx () Funded by: CZI, EMBO, ERC, FCT, H2021 and H2022 News: Tempo.pt, Nouvelles du monde and RTP DOI: 10.1038/s41592-024-02562-6 |
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The rise of data‐driven microscopy powered by machine learning Leonor Morgado, Estibaliz Gómez‐de‐Mariscal, Hannah S. Heil, Ricardo Henriques Review published in Journal of Microscopy, March 2024 Technologies: NanoJ () and NanoJ-Fluidics () Funded by: CZI, EMBO, ERC, FCT, H2021 and H2022 DOI: 10.1111/jmi.13282 |
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Harnessing artificial intelligence to reduce phototoxicity in live imaging Estibaliz Gómez-de-Mariscal, Mario Del Rosario, Joanna W. Pylvänäinen, Guillaume Jacquemet, Ricardo Henriques Perspective published in Journal of Cell Science, February 2024 Technologies: BioImage Model Zoo (), CARE (), DeepBacs (), NanoJ-eSRRF (), NanoJ-SQUIRREL (), NanoJ-SRRF () and ZeroCostDL4Mic () Funded by: CZI, EMBO, ERC, H2021 and H2022 DOI: 10.1242/jcs.261545 |
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News
- 2025-02-28: News outlet EUTOPIA highlights Saraiva et al. Nature Methods 2025 [external link]
- 2025-01-25: News outlet Tempo.pt highlights Saraiva et al. Nature Methods 2025 [external link]
- 2025-01-09: News outlet Nouvelles du monde highlights Saraiva et al. Nature Methods 2025 [external link]
- 2025-01-06: News outlet RTP highlights Saraiva et al. Nature Methods 2025 [external link]