Home

DeepBugs

Analyzing bacterial bioimages with deep learning using open-source tools

Our mision:

Deep learning is an amazing technology that has proven to possess great potential for bioimage analysis. Yet, bacteriologists make only limited use of this technology. To change this, we created several datasets of bacterial bioimages and used DL for different image analysis tasks.

Our work is based on the ZeroCostDL4Mic platform, as it provides simple access to different DL networks and gives you free GPU computing power! For this it employs the Google Colab platform.

Notebooks used in our work:

(notebooks are implementations provided by the ZeroCostDL4Mic platform. Further documentation can be found on the ZeroCostDL4Mic wiki)

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
StarDist (2D)	here and here	Nuclei segmentation	here
SplineDist	[here][SplineDist_link]	Instance segmentation	Coming soon!
Noise2Void (2D)	here	Denoising	here
CARE (2D)	here	Denoising	here
Label-free prediction (fnet) 2D	here	Artificial labelling	here
pix2pix	here	Paired Image-to-Image Translation	here
YOLOv2	here	Object detection (bounding boxes)	here

Tools

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

Contributors

• Christoph Spahn
• Romain F. Laine
• Pedro Matos Pereira
• Lucas von Chamier
• Seamus Holden
• Mia Conduit
• Guillaume Jacquemet
• Mike Heilemann
• Ricardo Henriques

You can find our preprint here ## add preprint link here