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

Tahk, Maris-Johanna; Torp, Jane; Ali, Mohammed A.S.; Fishman, Dmytro; Parts, Leopold; Grätz, Lukas; Müller, Christoph; Keller, Max; Veiksina, Santa; Laasfeld, Tõnis; Rinken, Ago
NameSizeDescription
Ilastik training.zip514.8MbImages used for training Ilastik random forest models
U-Net3 training.zip740.2MbImages used for training U-Net3 based models
README.txt5.716KbREADME
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Date
2022-01-13
URI
https://datadoi.ee/handle/33/431
https://doi.org/10.23673/re-305
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Abstract
The "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 balannced version of the ground truth....  Show more  Show less
Keyword
Deep convolutional neural networks; bright-field microscopy; fluorescence microscopy; Random forest; ground truth
Item type
info:eu-repo/semantics/dataset
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info:eu-repo/semantics/openAccess
Except where otherwise noted, this item's license is described as info:eu-repo/semantics/openAccess