DeepImageJ has been updated to DeepImageJ 2.1. The format of the models in previous versions are not compatible with DeepImageJ 2.1. Please, try to update your models using DeepImageJ Build Bundled Model or do not update DeepImageJ in Fiji using the Update Sites until you can update your models. Contact us if you have any question!

DeepImageJ is a user-friendly plugin that enables the use of a variety of pre-trained deep learning models in ImageJ and Fiji. The plugin bridges the gap between deep learning and standard life-science applications. DeepImageJ does not require any deep learning expertise.



DeepImageJ trained models

U-Net Pancreatic Segmentation

Binary SegmentationPhase Contrast

Data: Cell Tracking Challenge
Traning: deepImageJ & I. Arganda-Carreras

U-Net Glioblastoma Segmentation

Binary SegmentationPhase Contrast

Data: Cell Tracking Challenge
Traning: João Luis Soares Lopes (EPFL)

MU-Lux (CTC) PhC-C2DL-PSC cells

Instance SegmentationPhase Contrast

F. Lux & P. Matula, arXiv, 2020

FRU-Net sEV Segmentation

Instance SegmentationTEM

Estibaliz Gómez-de-Mariscal et al., Science Reports, 2019.

DEFCoN density map estimation

Density estimationSMLM

Baptiste Ottino et al. (EPFL)

Widefield TxRED Super-resolution

Super-resolutionFluorescence

Hongda Wang et al., Nature Methods, 2019.

U-Net HeLa Segmentation

Instance SegmentationDIC

Data: Cell Tracking Challenge
Traning: João Luis Soares Lopes (EPFL)

Widefield DAPI Super-resolution

Super-resolutionFluorescence

Hongda Wang et al., Nature Methods, 2019.

Widefield FITC Super-resolution

Super-resolutionFluorescence

Hongda Wang et al., Nature Methods, 2019.

Jones Virtual Staining

Virtual LabellingLight Transmission

Yair Rivenson et al., Nature biomedical engineering, 2019.

MT3 Virtual Staining

Virtual LabellingLight Transmission

Yair Rivenson et al., Nature biomedical engineering, 2019.

A first selection of state-of-the-art models from various groups has been made available in the BioImage Model Zoo. Beyond its direct use, we expect deepImageJ to contribute to the spread and validation of deep learning models in life-science applications.



New functionalities of deepImageJ 2.1

  • DeepImageJ is a compatible consumer of the trained models in the BioImage Model Zoo🦒.
  • From image-to-image in 2D to image-to-any: 3D image processing, image classification, object detection and instance segmentation.
  • GPU support.
  • Compatibility with PyTorch for the first time in ImageJ/Fiji.
  • Support new formats for pre- and post-processing: ImageJ macros and Java.

References



FAQ

The Image.sc Forum is the main discussion channel for deepImagej, hence we recommend to use it for any question or curisity related to it. Use a tag such as "deepimagej" so we can go through your questions. You will find already an extended Q&A post about Machine Learning and deepImageJ: [NEUBIAS Academy@Home] Webinar “Machine Learning/Deep Learning/DeepImageJ” + QUESTIONS & ANSWERS .



Connections with deepImageJ:



Getting close to the community

External tutorials & courses that use deepImageJ



News & Social media

DeepImageJ in ELMI 2021!!

SAVE THE DATE: 22-25 June, 2021!

REGISTER AND SAVE THE DATE: 26 November, 2020!

Workshop at SPAOM 2020: Practical Applications of Deep learning for Bioimage Analysis. Deep Learning, ZeroCostDL4Mic and DeepImageJ for 75 minutes!


REGISTER AND SAVE THE DATE: 21 April, 2020!


Live webinar in NEUBIAS Academy @Home: Introduction to Machine Learning and DeepImageJ, by Dr. Ignacio Arganda-Carreras, University of the Basque Country (UPV/EHU), Spain.



DeepImageJ is going to NEUBIAS 2020!






Ecole Polytechnique Fédérale de Lausanne
Initiative EPFL Center for Imaging
Biomedical Imaging Group


GitHub repository


Universidad Carlos III de Madrid
Bioengineering and Aerospace Engineering Department