DeepBacs – Escherichia coli MreB denoising dataset and CARE model

Training and test images of MreB-sfGFPsw expressing E. coli cells for image denoising, as well as a trained CARE model.

Additional information can be found on our github wiki.

The example images show confocal images of labelled E. coli MreB filaments at low and high SNR.

Training and test dataset:

Data type: Paired microscopy images (fluorescence)

Microscopy data type: Confocal fluorescence images

Microscope: Leica SP8 confocal microscope with a 1.40 NA 63x oil immersion objective 

Cell type: E. coli strain NO34 expressing MreB-sfGFPsw fusion protein (kindly provided by Zemer Gitai) 

File format: .tif (16-bit)

Image size: 512x512 (Pixel size: 45 nm)

The CARE 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 100 epochs (600 steps/epoch) on 1500 paired image patches (image dimensions: (512 x 512 px²), patch size: (64 x 64 px²), 50 patches/image) with a batch size of 8 and a laplace loss function, using the CARE 2D ZeroCostDL4Mic notebook (v 1). Key python packages used include tensorflow (v 0.1.12), Keras (v2.3.1), csbdeep (v 0.6.3), numpy (v 1.21.5), cuda (v 11.1.105). The training was accelerated using a Tesla K80 GPU and data was augmented by a factor of 4 using rotation and flipping.

The model weights can be used with the ZeroCostDL4Mic CARE 2D notebook and the CSBDeep Fiji plugin.

Author(s): Christoph Spahn1,2, Mike Heilemann1,3

Contact email: [e-mail]

Affiliation(s): 

1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany

2) ORCID: 0000-0001-9886-2263 

3) ORCID: 0000-0002-9821-3578