UT-GPCR002 Machine learning models for CHO-K1 cell segmentation from fluorescence and bright-field microscopy images

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
RF-BF-1.zip466.8MbRandom forest based bright-field image cell segmentation model as Ilastik model
RF-BF-2.zip1022.MbRandom forest based bright-field image cell segmentation model as Ilastik model
U-Net3-BF-1.zip13.66MbU-Net3 based bright-field image cell segmentation model as Keras model
U-Net3-FL-1.zip14.23MbU-Net3 based fluorescence image cell segmentation model as Keras model
RF-FL-1.zip153.9MbRandom forest based fluorescence image cell segmentation model as Ilastik model
README.txt5.514KbREADME
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Date
2022-01-13
URI
https://datadoi.ee/handle/33/430
https://doi.org/10.23673/re-304
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Abstract
The "UT-GPCR002 Machine learning models for CHO-K1 cell segmentation from fluorescence and bright-field microscopy images" dataset contains the machine learning model files for CHO-K1 cell segmentation from fluorescence and bright-field microscopy images. Random forest-based models are implemented as Ilastik projects while deep-learning models are implemented in Keras.
Keyword
U-Net; Random forest; Cell segmentation; Ilastik
Item type
Model
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