DeepBacs – Bacillus subtilis fluorescence segmentation dataset

Training and test images of live <em>B. subtilis </em>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 <em>B. subtilis </em>cells expressing FtsZ-GFP and the manually annotated segmentation mask. <strong>Data type</strong>: Paired fluorescence and segmented mask images <strong>Microscopy data type</strong>: 2D widefield images (fluorescence) <strong>Microscope</strong>: 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) <strong>Cell type</strong>: <em>B. subtilis</em> strain SH130 grown under agarose pads <strong>File format</strong>: .tiff (8-bit) or .png (8-bit) For segmented masks, binary masks are used for training of CARE/U-Net models, 8-bit .tif ROI maps for training of StarDist models and .png images for training of pix2pix models <strong>Image size</strong>: 1024 x 1024 px² (Pixel size: 65 nm) <strong>Image preprocessing</strong>: 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 <strong>Author(s)</strong>: Mia Conduit^1,2, Séamus Holden^1,3 <strong>Contact email</strong>: [e-mail] <strong>Affiliation</strong>: 1) Centre for Bacterial Cell Biology, Biosciences Institute, Newcastle University, NE2 4AX UK 2) ORCID: 0000-0002-7169-907X <strong>Associated publications</strong>: Whitley <em>et al</em>., 2021, Nature Communications, https://doi.org/10.15252/embj.201696235