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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.
| Running a ZeroCostDL4Mic notebook | Example data in ZeroCostDL4Mic | Romain's talk @ Aurox conference | Talk @ SPAOM | NEUBIAS webminar |
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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 that may not be stable yet.
We welcome network contributions from the research community. If you wish to contribute, please read our guidelines first.
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.
| Network | Paper(s) | Tasks | Status | Link to example training and test dataset | Direct link to the notebook in Colab |
|---|---|---|---|---|---|
| U-Net (2D) | here and here | Binary segmentation | Fully supported | here |  |
| U-Net (3D) | here | Binary segmentation | Fully supported | EPFL dataset | |
| U-Net (2D) multilabel | here and here | Semantic segmentation | Under beta-testing | here | |
| DenoiSeg | here | Joint denoising and binary segmentation | Fully supported | Available soon | |
| StarDist (2D) | here and here | Instance segmentation | Fully supported | here | |
| StarDist (3D) | here and here | Instance segmentation | Fully supported | from Stardist github | |
| Cellpose (2D and 3D) | here | Instance segmentation (Cells or Nuclei) | Fully supported | Coming soon! | |
| SplineDist (2D) | here | Instance segmentation | Fully supported | here | |
| EmbedSeg (2D) | here | Instance segmentation | Under beta-testing | here | |
| MaskRCNN (2D) | here | Instance segmentation | Under beta-testing | Coming soon! | |
| Interactive Segmentation - Kaibu (2D) | here | Interactive instance segmentation | Under beta-testing | Coming soon! | |
| Network | Paper(s) | Tasks | Status | Link to example training and test dataset | Direct link to the notebook in Colab |
|---|---|---|---|---|---|
| Noise2Void (2D) | here | Self-supervised denoising | Fully supported | here | |
| Noise2Void (3D) | here | Self-supervised denoising | Fully supported | here | |
| CARE (2D) | here | Supervised denoising | Fully supported | here | |
| CARE (3D) | here | Supervised denoising | Fully supported | here | |
| 3D-RCAN | here | Supervised denoising | Under beta-testing | here | |
| DecoNoising (2D) | here | Self-supervised denoising | Under beta-testing | here | |
| Network | Paper(s) | Tasks | Status | Link to example training and test dataset | Direct link to the notebook in Colab |
|---|---|---|---|---|---|
| Deep-STORM | here | Single Molecule Localization Microscopy (SMLM) image reconstruction from high-density emitter data | Fully supported | Training data simulated in the notebook or available from here | |
| DFCAN | here | image upsampling | Under beta-testing | here | |
| Network | Paper(s) | Tasks | Status | Link to example training and test dataset | Direct link to the notebook in Colab |
|---|---|---|---|---|---|
| YOLOv2 | here | Object detection (bounding boxes) | Fully supported | here | |
| Detectron2 | here | Object detection (bounding boxes) | Under beta-testing | here | |
| RetinaNet | here | Object detection (bounding boxes) | Under beta-testing | here | |
| Network | Paper(s) | Tasks | Status | Link to example training and test dataset | Direct link to the notebook in Colab |
|---|---|---|---|---|---|
| Label-free prediction (fnet) 2D | here | Artificial labelling | Under beta-testing | Coming soon | |
| Label-free prediction (fnet) 3D | here | Artificial labelling | Fully supported | here | |
| CycleGAN | here | Unpaired Image-to-Image Translation | Fully supported | here | |
| pix2pix | here | Paired Image-to-Image Translation | Fully supported | here | |
| Network | Paper(s) | Tasks | Status | Link to example training and test dataset | Direct link to the notebook in Colab |
|---|---|---|---|---|---|
| DRMIME | here | Affine or perspective image registration | Under beta-testing | Coming soon! | |
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 the notebook in Colab |
|---|---|---|---|---|
| StarDist (2D) with DeepImageJ export | StarDist: here and here, and DeepImageJ | Nuclei segmentation | here | |
| 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 | |
| U-Net (2D) with DeepImageJ export | U-Net and DeepImageJ | Segmentation | ISBI challenge or here | |
| U-Net (3D) with DeepImageJ export | 3D U-Net and DeepImageJ | Segmentation | EPFL dataset | |
| Network | Paper(s) | Task | Status | Link to example training and test dataset | Direct link to the notebook in Colab |
|---|---|---|---|---|---|
| Augmentor | here | Image augmentation | Fully supported | None | |
| Quality Control | here | Error mapping and quality metrics estimation | Fully supported | None | |
| Network | Paper(s) | Aim | Link to the project | Direct link to the notebook in Colab |
|---|---|---|---|---|
| DeepBacs | here | Toolbox to use Deep Learning to analyse microscopy images of bacteria | here | None |
| CAFI - DAIN | here | Content-aware frame interpolation using DAIN | here | |
| CAFI - ZoomingSlowMo | here | Content-aware frame interpolation using ZoomingSlowMo | here | |
| DECODE | here | Single Molecule Localization Microscopy (SMLM) image reconstruction | here | |
| EM-stellar | here | Electron microscopy image segmentation | here | |
- Lucas von Chamier
- Johanna Jukkala
- Christoph Spahn
- Martina Lerche
- Sara Hernández-Pérez
- Pieta K. Mattila
- Eleni Karinou
- Seamus Holden
- Ahmet Can Solak
- Alexander Krull
- Tim-Oliver Buchholz
- Florian Jug
- Loïc A Royer
- Mike Heilemann
- Romain F. Laine
- Guillaume Jacquemet
- Ricardo Henriques