DeepImageJ: A User-Friendly Environment to Run Deep Learning Models in ImageJ
E. Gómez-de-Mariscal, C. García-López-de-Haro, W. Ouyang, L. Donati, E. Lundberg, M. Unser, A. Muñoz-Barrutia, D. Sage
Nature Methods—Techniques for Life Scientists and Chemists, vol. 18, no. 10, pp. 1192–1195, October 2021.
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.
@ARTICLE(http://bigwww.epfl.ch/publications/gomezdemariscal2102.html,
AUTHOR="G{\'{o}}mez-de-Mariscal, E. and
Garc{\'{i}}a-L{\'{o}}pez-de-Haro, C. and Ouyang, W. and Donati, L.
and Lundberg, E. and Unser, M. and Mu{\~{n}}oz-Barrutia, A. and
Sage, D.",
TITLE="{DeepImageJ}: {A} User-Friendly Environment to Run Deep Learning
Models in {ImageJ}",
JOURNAL="Nature Methods---Techniques for Life Scientists and Chemists",
YEAR="2021",
volume="18",
number="10",
pages="1192--1195",
month="October",
note="")