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001     1045534
005     20250821202242.0
024 7 _ |a 10.5281/ZENODO.6497714
|2 doi
037 _ _ |a FZJ-2025-03537
100 1 _ |a Scherr, Tim
|0 0000-0001-8755-2825
|b 0
245 _ _ |a microbeSEG dataset
260 _ _ |c 2022
|b Zenodo
336 7 _ |a MISC
|2 BibTeX
336 7 _ |a Dataset
|b dataset
|m dataset
|0 PUB:(DE-HGF)32
|s 1755777891_5036
|2 PUB:(DE-HGF)
336 7 _ |a Chart or Table
|0 26
|2 EndNote
336 7 _ |a Dataset
|2 DataCite
336 7 _ |a DATA_SET
|2 ORCID
336 7 _ |a ResearchData
|2 DINI
520 _ _ |a 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.
536 _ _ |a 2171 - Biological and environmental resources for sustainable use (POF4-217)
|0 G:(DE-HGF)POF4-2171
|c POF4-217
|f POF IV
|x 0
588 _ _ |a Dataset connected to DataCite
650 _ 7 |a cell segmentation
|2 Other
650 _ 7 |a omero
|2 Other
650 _ 7 |a microbeSEG
|2 Other
700 1 _ |a Seiffarth, Johannes
|b 1
700 1 _ |a Wollenhaupt, Bastian
|b 2
700 1 _ |a Neumann, Oliver
|0 0000-0003-4438-300X
|b 3
700 1 _ |a Schilling, Marcel P.
|0 0000-0001-7366-2134
|b 4
700 1 _ |a Kohlheyer, Dietrich
|0 P:(DE-Juel1)140195
|b 5
700 1 _ |a Scharr, Hanno
|0 P:(DE-Juel1)129394
|b 6
700 1 _ |a Nöh, Katharina
|0 P:(DE-Juel1)129051
|b 7
700 1 _ |a Mikut, Ralf
|0 0000-0001-9100-5496
|b 8
773 _ _ |a 10.5281/ZENODO.6497714
909 C O |o oai:juser.fz-juelich.de:1045534
|p VDB
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
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910 1 _ |a Forschungszentrum Jülich
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|6 P:(DE-Juel1)129394
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 7
|6 P:(DE-Juel1)129051
913 1 _ |a DE-HGF
|b Forschungsbereich Erde und Umwelt
|l Erde im Wandel – Unsere Zukunft nachhaltig gestalten
|1 G:(DE-HGF)POF4-210
|0 G:(DE-HGF)POF4-217
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-200
|4 G:(DE-HGF)POF
|v Für eine nachhaltige Bio-Ökonomie – von Ressourcen zu Produkten
|9 G:(DE-HGF)POF4-2171
|x 0
920 1 _ |0 I:(DE-Juel1)IBG-1-20101118
|k IBG-1
|l Biotechnologie
|x 0
920 1 _ |0 I:(DE-Juel1)IAS-8-20210421
|k IAS-8
|l Datenanalyse und Maschinenlernen
|x 1
980 _ _ |a dataset
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)IBG-1-20101118
980 _ _ |a I:(DE-Juel1)IAS-8-20210421
980 _ _ |a UNRESTRICTED

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