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

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

• Elias Nehme
• Lucien Weiss
• Yoav Shechtman
• Christophe Leterrier
• Daniel Krentzel
• Martin Jones
• Wei Ouyang
• Estibaliz Gómez de Mariscal
• Erlantz Calvo
• Ignacio Arganda-Carreras
• Amin Rezaei
• Ainhoa Serrano

• Martin Weigert
• Uwe Schmidt