B. subtilis and E. coli cell segmentation dataset consisting of test data annotated by three experts (test), data annotated manually by a single microbeSEG user within 30 minutes (30min-man), data annotated manually by a single microbeSEG user within 30 minutes and data annotated with microbeSEG pre-labeling with 15 minutes manual correction time (30min-man_15min-pre, includes the 30min-man dataset). Images, instance segmentation masks and image-segmentation overlays are provided. All images are crops of size 320px x 320px. Annotations were made with ObiWan-Microbi. Data acquisition The phase contrast images of growing B. subtilis and E. coli colonies were acquired with a fully automated time-lapse microscope setup (TI Eclipse, Nikon, Germany) using a 100x oil immersion objective (Plan Apochromat λ Oil, N.A. 1.45, WD 170 µm, Nikon Microscopy). Time-lapse images were taken every 15 minutes for B. subtilis and every 20 minutes for E. coli. Cultivation took place inside a special microfluidic cultivation device. Resolution: 0.07μm/px for B. subtilis und 0.09μm/px for E. coli. microbeSEG import For the use with microbeSEG, create or select a new training set within the software and use the training data import functionality. Best import train data with the "train" checkbox checked, validation data with the "val" checkbox checked, and test data with the "test" checkbox checked. Since the images are already normalized, the "keep normalization" functionality can be used.

Please cite: Tim Scherr, Johannes Seiffarth, Bastian Wollenhaupt, Oliver Neumann, Marcel P. Schilling, Dietrich Kohlheyer, Hanno Scharr, Katharina Nöh, and Ralf Mikut (2022). microbeSEG: A deep learning software tool with OMERO data management for efficient and accurate cell segmentation. PLoS ONE 17(11): e0277601, doi:10.1371/journal.pone.0277601.