ZeroCostDL4Mic / DeepBacs - Multi-label U-Net training dataset (Bacillus subtilis) and pretrained model

Training and test images of live B. subtilis cells expressing FtsZ-GFP for the task of segmentation. Additional information can be found on this github wiki. The example shows the fluorescence widefield image of live B. subtilis cells expressing FtsZ-GFP, the manually annotated instance segmentation mask and the corresponding 2-label semantic segmentation mask used for model training. Training and test dataset Data type: Paired fluorescence and segmented mask images Microscopy data type: 2D widefield images (fluorescence) Microscope: Custom-built 100x inverted microscope bearing a 100x TIRF objective (Nikon CFI Apochromat TIRF 100XC Oil); images were captured on a Prime BSI sCMOS camera (Teledyne Photometrics) Cell type: B. subtilis strain SH130 grown under agarose pads File format: .tiff (8-bit) Image size: 1024 x 1024 px² (Pixel size: 65 nm) Image preprocessing: Images were denoised using PureDenoise and resulting 32-bit images were converted into 8-bit images after normalizing to 1% and 99.98% percentiles. Images were manually annotated using the Labkit Fiji plugin and mask images with labeled cytosol and cell boundaries were created using a custom Fiji macro (see our github repository). Multi-label U-Net model: The U-Net (2D) multilabel model was generated using the ZeroCostDL4Mic platform (Chamier & Laine et al., 2021). It was trained from scratch for 200 epochs on 733 paired image patches (image dimensions: (1024 x 1024 px²), patch size: (256 x 256 px²)) with a batch size of 8 and a categorical_crossentrop loss function, using the U-Net (2D) multilabel ZeroCostDL4Mic notebook (v 1) (Chamier & Laine et al., 2021). Key python packages used include tensorflow (v 0.1.12), Keras (v 2.3.1), numpy (v 1.19.5), cuda (v 11.1.105). The training was accelerated using a Tesla P100GPU. Author(s): Mia Conduit1,2, Séamus Holden1,3 Contact email: Seamus.Holden@newcastle.ac.uk Affiliation: 1) Centre for Bacterial Cell Biology, Biosciences Institute, Newcastle University, NE2 4AX UK 2) ORCID: 0000-0002-7169-907X Associated publications: Whitley et al., 2021, Nature Communications, https://doi.org/10.15252/embj.20169623