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DeepImageJ is a user-friendly environment that enables the use of a variety of pre-trained deep learning models in ImageJ. It bridges the gap between deep learning and standard life-science applications. DeepImageJ provides different plugins to guide ImageJ users while using trained deep learning models for their image analysis. Through DeepImageJ it is possible to perform a variety of common image processing tasks such as image classification, binary / semantic / instance / panoptic segmentation, denoising, deconvolution, virtual staining, regression, or super-resolution.
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.
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Cite the appropriate work that is bundled into DeepImageJ (deep learning model developers and/or trainers).
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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). https://doi.org/10.1038/s41592-021-01262-9
Find all the information about the DeepImageJ project at https://deepimagej.github.io
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DeepImageJ is written in ImageJ1, so it can be used in either ImageJ1, ImageJ2, or Fiji.
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For Fiji and ImageJ2 users, DeepImageJ makes use of the TensorFlow version manager developed by Deborah Schmidt and Curtis Reuden. Source code and further information&discussion at the Scientific Community Image Forum.
Operating systems (same requirements as for ImageJ/Fiji software).
- Windows
- Mac OSX
- Linux.
- Find it at GitHub releases (https://github.com/deepimagej.github.io/deepimagej-plugin/releases).
