DeepImageJ is a user-friendly plugin that enables the use of a variety of pre-trained deep learning models in ImageJ. The plugin bridges the gap between deep learning and standard life-science applications. DeepImageJ runs image-to-image operations on a standard CPU-based computer or on a GPU, and does not require any deep learning expertise.

Start using it with the guidelines in the Wiki.

Find further information about DeepImageJ environment at https://deepimagej.github.io/deepimagej

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.

• If you used one of the material provided within DeepImageJ such as trained models or Python notebooks, cite their authors' work.
• 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).

@article{gomez2021deepimagej,
  title={DeepImageJ: A user-friendly environment to run deep learning models in ImageJ},
  author={G{\'o}mez-de-Mariscal, Estibaliz and Garc{\'i}a-L{\'o}pez-de-Haro, Carlos and Ouyang, Wei and Donati, Laur{\`e}ne and Lundberg, Emma and Unser, Michael and Mu{\~{n}}oz-Barrutia, Arrate and Sage, Daniel},
  journal={Nature Methods},
  year={2021},
  volume={18},
  number={10},
  pages={1192-1195},
  URL = {https://doi.org/10.1038/s41592-021-01262-9},
  doi = {10.1038/s41592-021-01262-9}
}