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@MISC{Scherr:1045534,
      author       = {Scherr, Tim and Seiffarth, Johannes and Wollenhaupt,
                      Bastian and Neumann, Oliver and Schilling, Marcel P. and
                      Kohlheyer, Dietrich and Scharr, Hanno and Nöh, Katharina
                      and Mikut, Ralf},
      title        = {microbe{SEG} dataset},
      publisher    = {Zenodo},
      reportid     = {FZJ-2025-03537},
      year         = {2022},
      abstract     = {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.},
      keywords     = {cell segmentation (Other) / omero (Other) / microbeSEG
                      (Other)},
      cin          = {IBG-1 / IAS-8},
      cid          = {I:(DE-Juel1)IBG-1-20101118 / I:(DE-Juel1)IAS-8-20210421},
      pnm          = {2171 - Biological and environmental resources for
                      sustainable use (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2171},
      typ          = {PUB:(DE-HGF)32},
      doi          = {10.5281/ZENODO.6497714},
      url          = {https://juser.fz-juelich.de/record/1045534},
}