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UT-GPCR004 CHO-K1 cell line bright-field and fluorescence microscopy and corresponding segmentation ground truth

dc.contributor.authorTahk, Maris-Johanna
dc.contributor.authorTorp, Jane
dc.contributor.authorAli, Mohammed A.S.
dc.contributor.authorFishman, Dmytro
dc.contributor.authorParts, Leopold
dc.contributor.authorGrätz, Lukas
dc.contributor.authorMüller, Christoph
dc.contributor.authorKeller, Max
dc.contributor.authorVeiksina, Santa
dc.contributor.authorLaasfeld, Tõnis
dc.contributor.authorRinken, Ago
dc.date.accessioned2022-01-16T19:50:21Z
dc.date.available2022-01-16T19:50:21Z
dc.date.issued2022-01-13
dc.identifier.urihttps://datadoi.ee/handle/33/431
dc.identifier.urihttps://doi.org/10.23673/re-305
dc.description.abstractThe "UT-GPCR004 CHO-K1 cell line bright-field and fluorescence microscopy and corresponding segmentation ground truth" dataset contains the raw microscopy images, corresponding bright-field Z-stack based contrast-enhanced images and the corresponding manual ground truth for fluorescence and bright-field images as well as ground truth generated from the fluorescence images using either random forest-based Ilastik model or U-Net3 based deep learning model. The dataset also contains the class balanced version of the ground truth.en
dc.formatTIFFen
dc.formatHDF5en
dc.formatpngen
dc.language.isoenen
dc.publisherUniversity of Tartu, Institute of Chemistry, Chair of Bioorganic chemistryen
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectDeep convolutional neural networksen
dc.subjectbright-field microscopyen
dc.subjectfluorescence microscopyen
dc.subjectRandom foresten
dc.subjectground truthen
dc.titleUT-GPCR004 CHO-K1 cell line bright-field and fluorescence microscopy and corresponding segmentation ground truthen
dc.typeinfo:eu-repo/semantics/dataseten
dc.relation.iscitedbyhttps://doi.org/10.1101/2021.12.22.473643en


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