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Deep learning is a powerful tool in many areas. In the last years, it also gained large popularity in the field of bioimage analysis. Yet, bacteriologists make only limited use of this technology, although there is great potential. To leverage the use of DL in microbiology, we created several datasets of bacterial bioimages and tested DL networks 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.
Scale bars are 2 µm
We are always happy for new ideas and examples. Please feel free to contact us if you want to contribute data and models or want to discuss any useful applications of DL for microbiology!
List of tasks with links to the respective pages:
All notebooks are implementations provided by the ZeroCostDL4Mic platform. Further documentation can be found on the ZeroCostDL4Mic wiki
| Network | Paper(s) | Task | Link to DeepBac example training and test dataset | Direct link to notebook in Colab |
|---|---|---|---|---|
| U-Net (2D) | here and here | Segmentation | here |  |
| U-Net (2D) multilabel | here and here | Semantic segmentation | here | |
| StarDist (2D) | here and here | Instance segmentation of star-convex objects | here | |
| SplineDist | here | Instance segmentation | here | |
| Noise2Void (2D) | here | Denoising | B. subtilis FtsZ and E. coli nucleoid | |
| CARE (2D) | here | Denoising | E. coli nucleoid | |
| Label-free prediction (fnet) 2D | here | Artificial labelling | Artificial labelling | |
| pix2pix | here | Paired Image-to-Image Translation | B. subtilis segmentation | |
| YOLOv2 | here | Object detection (bounding boxes) | Growth stage and Antibiotic phenotyping | |
| 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 | |
- Christoph Spahn
- Romain F. Laine
- Pedro Matos Pereira
- Estibaliz Gómez de Mariscal
- Lucas von Chamier
- Seamus Holden
- Mia Conduit
- Mariana Gomes de Pinho
- Guillaume Jacquemet
- Mike Heilemann
- Ricardo Henriques
You can find our preprint here https://doi.org/10.1101/2021.11.03.467152
@article{cspahn2021,
title={DeepBacs: Bacterial image analysis using open-source deep learning approaches},
author={Spahn, Christoph and Laine, Romain F. and Matos Pereira, Pedro and von Chamier, Lucas and Conduit, Mia and G{\'o}mez-de-Mariscal, Estibaliz and Gomes de Pinho, Mariana and Jacquemet, Guillaume and Holden, S{\'{e}}amus and Heilemann, Mike and Henriques, Ricardo},
journal={bioRxiv},
year={2021},
doi = {10.1101/2021.11.03.467152},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2021/11/03/2021.11.03.467152}
}