Cutting-edge super-resolution image analysis in napari through NanoJ


Funding Image
Agency: CZI
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


NanoPyx NanoPyx Newest update: 2024-07-11
NanoJ NanoJ Newest update: 2024-03-22
NanoJ-eSRRF NanoJ-eSRRF Newest update: 2024-03-22
NanoJ-SRRF NanoJ-SRRF Newest update: 2024-03-22
NanoJ-SQUIRREL NanoJ-SQUIRREL Newest update: 2020-09-10

Supported publications


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, January 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
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, January 2024
Technologies: NanoJ and NanoJ-Fluidics
Funded by: CZI, EMBO, ERC, FCT, H2021 and H2022
DOI: 10.1111/jmi.13282
NanoPyx - super-fast bioimage analysis powered by adaptive machine learning
Bruno M. Saraiva, Inês M. Cunha, António D. Brito, Gautier Follain, Raquel Portela, Robert Haase, Pedro M. Pereira, Guillaume Jacquemet, Ricardo Henriques
Preprint published in bioRxiv, January 2023
Technologies: NanoJ, NanoJ-eSRRF, NanoJ-SQUIRREL, NanoJ-SRRF, NanoJ-VirusMapper and NanoPyx
Funded by: CZI, EMBO, ERC, FCT, H2021 and H2022
DOI: 10.1101/2023.08.13.553080