Training and test images of live E. coli cells imaged under bright field for the task of segmentation. Additional information can be found on this github wiki. The example shows a bright field image of live E. coli cells of an overnight culture and the manually annotated segmentation mask. Data type: Paired bright field and segmented mask images Microscopy data type: 2D bright field images recorded at 2 min interval Microscope: Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective Cell type: E. coli MG1655 wild type strain (CGSC #6300). File format: .tif (8-bit) Image size: 512 x 512 px² (106 nm / pixel), 19/15 individual frames (training/test dataset) 512 x 512 px² (106 nm / pixel), 7 regions of interest with 20 frames @ 2 min time interval (live-cell time series) Data annotation: Images were annotated using the Fiji freehand selection tool. Image preprocessing: Time series were stabilized using the Fiji plugin StackReg and the 480 x 480 px center region was cropped StarDist model The StarDist 2D model was trained from scratch for 200 epochs on 33 paired image patches (image dimensions: (512, 512 px²), patch size: (512 x 512 px²)) with a batch size of 2, 80 rays, grid size 1, 4-fold data augmentation and a mae loss function, using the StarDist 2D ZeroCostDL4Mic notebook (v 1.13) (von Chamier & Laine et al., 2020). 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. Model weights can be used with the ZeroCostDL4Mic StarDist 2D notebook or the Fiji StarDist plugin. Author(s): Christoph Spahn1,2, Mike Heilemann1,3 Contact email: christoph.spahn@mpi-marburg.mpg.de 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