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 runs image-to-image operations on a standard CPU-based computer and does not require any deep learning expertise.



Bundled 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 an online repository. Beyond its direct use, we expect deepImageJ to contribute to the spread and validation of deep learning models in life-science applications.



The project

The concept consists on bundling a trained TensorFlow model to additional information, so it can be used in ImageJ/Fiji as a standard plugin.



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 .



Coming soon

  • Compatibility with 🦒Bioimage.IO
  • GPU-connection
  • Image-to-any kind of output
  • Connection with ImJoy


Connections with deepImageJ:



Getting close to the community

External tutorials & courses that use deepImageJ



News & Social media


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 Imaging@EPFL
Biomedical Imaging Group


Universidad Carlos III de Madrid
Bioengineering and Aerospace Engineering Department