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DeepImageJ: A user-friendly environment to run deep learning models in ImageJ

Abstract

DeepImageJ is a user-friendly solution that enables the generic use of pre-trained deep learning models for biomedical image analysis in ImageJ. The deepImageJ environment gives access to the largest bioimage repository of pre-trained deep learning models (BioImage Model Zoo). Hence, nonexperts can easily perform common image processing tasks in life-science research with deep learning-based tools including pixel and object classification, instance segmentation, denoising or virtual staining. DeepImageJ is compatible with existing state of the art solutions and it is equipped with utility tools for developers to include new models. Very recently, several training frameworks have adopted the deepImageJ format to deploy their work in one of the most used softwares in the field (ImageJ). Beyond its direct use, we expect deepImageJ to contribute to the broader dissemination and reuse of deep learning models in life sciences applications and bioimage informatics.

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Fig. 1: DeepImageJ environment and scope.
Fig. 2: Functionalities of deepImageJ.

Data availability

All data that were used to generate the figures in this paper are available at https://deepimagej.github.io/deepimagej/.

Code availability

We used TensorFlow and PyTorch libraries for Python to create the in-house models shown in the figures. The deepImageJ plugin can be used in ImageJ and Fiji. The source code for the plugin together with its releases is provided at https://github.com/deepimagej/deepimagej-plugin. The pydeepimagej Python package is provided at https://github.com/deepimagej/pydeepimagej. The ImageJ macro files can be accessed at https://github.com/deepimagej/imagej-macros. All source code is under a BSD 2-Clause License. The web page https://deepimagej.github.io/deepimagej/provides free access to the ImageJ plugin, along with the bundled models and user guide for image processing.

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Acknowledgements

We thank P. M. Gordaliza, I. Arganda-Carreras (tested the beta-versions), D. F. González-Obando (tested the beta-versions), C. Rueden, S. Tosi, T. Pengo (tested the beta-versions), R. Henriques (ZeroCostDL4Mic), R. F. Laine (ZeroCostDL4Mic), G. Jacquemet (ZeroCostDL4Mic), D. Krentzel (ZeroCostDL4Mic) and C. Möhl (YAPIC) for the fruitful discussions and enriching feedback about the deepImageJ project. We would also like to thank NEUBIAS for supporting the project, the NEUBIAS symposium and NEUBIAS Academy@Home and P. Rasti and S. Bollmann for including deepImageJ in their tutorials. We would like specially to mention all the contributors and community partners at the BioImage Model Zoo for the time they have spent to get a cross-compatible model format. We acknowledge the support of Ministerio de Ciencia, Innovación y Universidades, Agencia Estatal de Investigación, under grant nos TEC2016-78052-R and PID2019-109820RB-I00, MINECO/FEDER, UE, co-financed by European Regional Development Fund (ERDF), ‘A way of making Europe’ (E.G.M., C.G.L.H., A.M.B.), and a 2017 Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation (A.M.B.). This work was also supported by the EPFL Center for Imaging (C.G.L.H., L.D., D.S., M.U.). We would like to thank the Science for Life Laboratory, Erling-Persson Foundation and Knut and Alice Wallenberg foundation (grant no. 2018.0172) (W.O., E.L.). We thank the program ‘Short Term Scientific Missions’ of NEUBIAS (network of European bioimage analysts) (E.G.M., C.G.L.H.). We also want to acknowledge the support of NVIDIA Corporation with the donation of the Titan X (Pascal) GPU card used for this research (A.M.B.).

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Contributions

E.G.-M. and C.G.-L.-H. contributed to the design of the experimental framework, and reviewed, trained and exported existing image processing methods. C.G.-L.-H. and D.S. developed and implemented the toolbox and worked on the supporting documentation with input from the rest of the authors. C.G.-L.-H. and W.O. built the connection between the toolbox and ImJoy. E.G.-M. wrote the code lines of the supplementary Python notebooks, Python library and ImageJ macros. E.G.M. and W.O. worked on the synchronization with the BioImage Model Zoo. E.G.-M., W.O. and L.D. wrote the manuscript with help from E.L., M.U., A.M.-B. and D.S. E.G.-M., A.M.-B. and D.S. created the website of deepImageJ. M.U., A.M.-B. and D.S. initiated the project. A.M.-B. and D.S. supervised the project. All the authors contributed to the conception of the study, the design of the experimental framework and took part in the literature review. All authors revised the manuscript.

Corresponding authors

Correspondence to Arrate Muñoz-Barrutia or Daniel Sage.

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The authors declare no competing interests.

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Peer review information Rita Strack was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Supplementary Information

Supplementary Figs. 1–6, Methods, User guide and Examples.

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Gómez-de-Mariscal, E., García-López-de-Haro, C., Ouyang, W. et al. DeepImageJ: A user-friendly environment to run deep learning models in ImageJ. Nat Methods 18, 1192–1195 (2021). https://doi.org/10.1038/s41592-021-01262-9

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