Mixed training and test images of S. aureus, E. coli and B. subtilis for cell segmentation using StarDist, as well as the trained StarDist model. Additional information can be found on this github wiki. Data type: Paired bright field / fluorescence and segmented mask images Microscopy data type: 2D widefield images; DIC and fluorescence for S. aureus, bright field images for E. coli, and fluorescence images for B. subtilis Microscopes: S. aureus: GE HealthCare Deltavision OMX system (with temperature and humidity control, 37��C) equipped with an Olympus 60x 1.42NA Oil immersion objective and 2 PCO Edge 5.5 sCMOS cameras (one for DIC, one for fluorescence) E.coli: Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective B. subtilis: 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 types: S. aureus strain JE2, E. coli MG1655 (CGSC #6300) and B. subtilis strain SH130; all grown under agarose pads File format: .tif (8-bit and 16-bit) Image size: 512 x 512 px�� @ 80 nm pixel size (S. aureus); 1024 x 1024 px�� @ 79 nm pixel size (E. coli); 1024 x 1024 px�� @ 65 nm pixel size (B. subtilis) Image preprocessing: S. aureus: Raw images were manually annotated by drawing ellipses in the NR fluorescence image and segmented images were created using the LOCI plugin (���ROI Map���). For training, images and masks were quartered into four 256 x 256 px�� patches. E. coli: Raw images were recorded in 16-bit mode (image size 512x512 px�� @ 158 nm/px). Images were upscaled with a factor of 2 (no interpolation) to enable generation of higher-quality segmentation masks. B. subtilis: 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 StarDist model: The StarDist 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 200 epochs (120 steps/epoch) on 155 paired image patches (image dimensions: (1024, 1024), patch size: (256,256)) with a batch size of 4, 10% validation data, 64 rays on grid 2, a learning rate of 0.0003 and a mae loss function, using the StarDist 2D ZeroCostDL4Mic notebook (v 1.12.2). Key python packages used include tensorflow (v 0.1.12), Keras (v 2.3.1), csbdeep (v 0.6.1), numpy (v 1.19.5), cuda (v 11.0.221). The training was accelerated using a Tesla P100GPU. The dataset was augmented by a factor of 3. The model weights can be used in the ZeroCostDL4Mic StarDist 2D notebook, the StarDist Fiji plugin or the TrackMate Fiji plugin (v7+). Author(s): Christoph Spahn1,2, Mike Heilemann1,3, Mia Conduit4, S��amus Holden4,5, Pedro Matos Pereira6,7, Mariana Pinho6,8 Contact email: christoph.spahn@mpi-marburg.mpg.de, Seamus.Holden@newcastle.ac.uk, pmatos@itqb.unl.pt and mgpinho@itqb.unl.pt 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 4) Centre for Bacterial Cell Biology, Biosciences Institute, Newcastle University, NE2 4AX UK 5) ORCID: 0000-0002-7169-907X 6) Bacterial Cell Biology, Instituto de Tecnologia Qu��mica e Biol��gica Ant��nio Xavier, Universidade Nova de Lisboa, Oeiras, Portugal 7) ORCID: 0000-0002-1426-9540 8) ORCID: 0000-0002-7132-8842