RT Journal Article
SR Electronic
T1 DeepImageJ: A user-friendly environment to run deep learning models in ImageJ
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 799270
DO 10.1101/799270
A1 Estibaliz Gómez-de-Mariscal
A1 Carlos García-López-de-Haro
A1 Wei Ouyang
A1 Laurène Donati
A1 Emma Lundberg
A1 Michael Unser
A1 Arrate Muñoz-Barrutia
A1 Daniel Sage
YR 2021
UL http://biorxiv.org/content/early/2021/05/06/799270.abstract
AB DeepImageJ is a user-friendly solution that enables the generic use of pre-trained deep learn ing (DL) models for biomedical image analysis in ImageJ. The deepImageJ environment gives access to the largest bioimage repository of pre-trained DL models (BioImage Model Zoo). Hence, non-experts can easily perform common image processing tasks in life-science research with DL-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 train ing frameworks have adopted the deepImageJ format to deploy their work in one of the most used software in the field (ImageJ). Beyond its direct use, we expect deepImageJ to contribute to the broader dissemination and reuse of DL models in life-sciences applications and bioimage informatics.Competing Interest StatementThe authors have declared no competing interest.