About the plugin

• Source code

• How to use: Check the Documentation of deepImageJ 1.

• DeepImageJ splits into four different modules:

  DeepImageJ Run: Applies a convolutional neural network to an input image (it is macro-recordable).

  DeepImageJ Install Model: Installs deepImageJ compatible models from the BioImage Model Zoo, a given URL or a local path.

  DeepImageJ Validate: Quantitative comparison of a result image with a given ground truth image.

  DeepImageJ Releases: All releases of deepImageJ can be found here.

Conditions of use

The deepImageJ project is an open-source software (OSS) under the BSD 2-Clause License. All the resources provided here are freely available. As a matter of academic integrity, we strongly encourage users to include adequate references whenever they present or publish results that are based on the resources provided here.

References

• Cite the appropriate TensorFlow network which is bundled into deepImageJ.
• E. Gómez-de-Mariscal, C. García-López-de-Haro, W. Ouyang, L. Donati, E. Lundberg, M. Unser, A. Muñoz-Barrutia, D. Sage, "DeepImageJ: A user-friendly environment to run deep learning models in ImageJ", Nat Methods 18, 1192–1195 (2021). DOI: https://doi.org/10.1038/s41592-021-01262-9
• W. Ouyang, F. Beuttenmueller, E. Gómez-de-Mariscal, C. Pape, T. Burke, C. Garcia-López-de-Haro, C. Russell, L. Moya-Sans, C. de-la-Torre-Gutiérrez, D. Schmidt, D. Kutra, M. Novikov, M. Weigert, U. Schmidt, P. Bankhead, G. Jacquemet, D. Sage, R. Henriques, A. Muñoz-Barrutia, E. Lundberg, F. Jug, A. Kreshuk, BioImage Model Zoo: A Community-Driven Resource for Accessible Deep Leaning in BioImage Analysis, BioRxiv (2022). DOI: https://doi.org/10.1101/2022.06.07.495102

Acknowledgements

• This work is supported by the Spanish Ministry of Economy and Competitiveness (TEC2015-73064-EXP, TEC2016–78052-R) and by a 2017 Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation.
• This work is part of the EPFL initiative imaging@EPFL.
• We thanks the program "Short Term Scientific Missions" of NEUBIAS (Network of european bioimage analysists).
• We also want to acknowledge the support of NVIDIA Corporation with the donation of the Titan X (Pascal) GPU card used for this research.
• Funded by the European Union’s Horizon Europe research and innovation programme under grant agreement number 101057970. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. AI4Life

Code of conduct

Please, note that deepImageJ aims to contribute to the biomedical image analysis community in a respectful and constructive manner, this being the only way to support a healthy open-source philosophy. For this reason, we encourage inclusive and positive behaviors when contributing to deepImageJ. We want to support an atmosphere of kindness, diversity and cooperation among the most specialized computer scientist and experts in life science to boost biomedical image analysis workflows. We seek contributions that preserve scientific integrity and robustness, so well-documented work, professional discussions, and proper scientific citation are a must. All those guidelines together define deepImagej's code of conduct.

Contact

• GitHub issue tracker: use it to report bugs or request new features.
• Image.sc Forum use it to share your feed-back with the community, to open new discussions or to ask for help. Please write about it in the forum with the tag "deepimagej" and we will answer you as soon as possible.
• You can also contact us by e-mail:
• Estibaliz Gómez de Mariscal
• Arrate Muñoz Barrutia
• Daniel Sage