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guijacquemet edited this page Nov 10, 2021 · 5 revisions

ZeroCostDL4Mic - What is it?

Overview

ZeroCostDL4Mic is a toolbox for the training and implementation of common Deep Learning approaches to microscopy imaging. It exploits the ease-of-use and access to GPU provided by Google Colab.

Training data can be uploaded to the Google Drive from where it can be used to train models using the provided Colab notebooks in a web-browser. Inference (predictions) on unseen data can then also be performed within the same notebook, therefore not requiring any local hardware or software set-up.

Want to see a short video demonstration?

Running a ZeroCostDL4Mic notebook Example data in ZeroCostDL4Mic Romain's talk @ Aurox conference Talk @ SPAOM

Implemented networks

ZeroCostDL4Mic provides fully annotated Google Colab optimised Jupyter Notebooks for popular pre-existing networks. These cover a range of important image analysis tasks (e.g. segmentation, denoising, restoration, label-free prediction). There are 3 types of implemented networks:

  • Fully supported - considered mature and considerably tested by our team.
  • Under beta-testing - an early prototype of networks which may not be stable yet.
  • Contributed - networks following the ZeroCostDL4Mic guidelines and contributed by community members. Although the core ZeroCostDL4Mic team does not maintain these networks, we synergise with the developers with the goal of providing researchers with a similar workflow experience and quality control.

How to get the notebooks and test datasets?

Both fully supported and beta-testing versions of the individual notebooks can be directly opened from GitHub into Colab by clicking one of the respective links in the table below. You will need to create a local copy to your Google Drive in order to save and modify the notebooks. Once opened in Colab, follow the instructions described in the specific notebook that you selected to install the relevant packages, load the training dataset, train, check on test datasets and perform inference and predictions on unseen data.

With the exception of the U-net training data, we provide training and test datasets that were generated by our labs. These can be downloaded from Zenodo using the various links below. The U-net data was obtained from the ISBI segmentation contest.

Fully supported networks

!! For U-net example dataset, it seems that the ISBI website is currently broken, so please use the alternative link provided (more info on this on the dedicated U-Net page) !!

Network Paper(s) Task Link to example training and test dataset Direct link to notebook in Colab
U-Net (2D) here and here Segmentation ISBI challenge or here Open In Colab
U-Net (3D) here Segmentation EPFL dataset Open In Colab
StarDist (2D) here and here Nuclei segmentation here Open In Colab
StarDist (3D) here and here Nuclei segmentation from Stardist github Open In Colab
Noise2Void (2D) here Denoising here Open In Colab
Noise2Void (3D) here Denoising here Open In Colab
CARE (2D) here Denoising here Open In Colab
CARE (3D) here Denoising here Open In Colab
Label-free prediction (fnet) 2D here Artificial labelling here Open In Colab
Label-free prediction (fnet) 3D here Artificial labelling here Open In Colab
Deep-STORM here Single Molecule Localization Microscopy (SMLM) image reconstruction from high-density emitter data Training data simulated in the notebook or available from here Open In Colab
CycleGAN here Unpaired Image-to-Image Translation here Open In Colab
pix2pix here Paired Image-to-Image Translation here Open In Colab
YOLOv2 here Object detection (bounding boxes) here Open In Colab

Under beta-testing

Network Paper(s) Task Link to example training and test dataset Direct link to notebook in Colab
DenoiSeg here Joint denoising and segmentation Available soon Open In Colab
3D-RCAN here Denoising Available soon Open In Colab
SplineDist here Instance segmentation Coming soon! Open In Colab
Detectron2 here Object detection (bounding boxes) here Open In Colab
RetinaNet here Object detection (bounding boxes) here Open In Colab
DRMIME here Affine or perspective image registration Coming soon! Open In Colab
Cellpose (2D) here Cells or Nuclei segmentation Coming soon! Open In Colab
DecoNoising (2D) here Denoising here Open In Colab
Interactive Segmentation - Kaibu here Interactive instance segmentation Coming soon! Open In Colab
MaskRCNN here Instance segmentation Coming soon! Open In Colab
U-Net (2D) multilabel here and here Semantic segmentation here Open In Colab

BioImage.io notebooks

Networks that are compatible with BioImage.IO and can be used in ImageJ via deepImageJ.

Network Paper(s) Task Link to example training and test dataset Direct link to notebook in Colab
StarDist (2D) with DeepImageJ export StarDist: here and here, and DeepImageJ Nuclei segmentation here Open In Colab
Deep-STORM with DeepImageJ export Deep-STORM and DeepImageJ Single Molecule Localization Microscopy (SMLM) image reconstruction from high-density emitter data Training data simulated in the notebook or available from here Open In Colab
U-Net (2D) with DeepImageJ export U-Net and DeepImageJ Segmentation ISBI challenge or here Open In Colab
U-Net (3D) with DeepImageJ export 3D U-Net and DeepImageJ Segmentation EPFL dataset Open In Colab

Tools

Network Paper(s) Task Link to example training and test dataset Direct link to the notebook in Colab
Augmentor here Image augmentation None Open In Colab
Quality Control Available soon Error mapping and quality metrics estimation None Open In Colab

Contributed

We welcome network contributions from the research community. If you wish to contribute, please read our guidelines first.

Available soon...

Example datasets

The figure below shows some of the representative datasets which we provide as examples to use the notebooks with. The description on how to acquire similar test datasets is described in the pages shown in the sidebar of this page, as well as in the Supplementary Information of our paper here.

Downloading notebooks code

If you want to get the latest fully supported and beta-testing releases of all the notebooks as a set of individual files, it can be downloaded from here. All the notebooks are included in the compressed folder.

Contributors

Developers and testers

Founding

Newcomers

Researchers providing guidance and recommendations