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

dc.contributor.author	Tahk, Maris-Johanna
dc.contributor.author	Torp, Jane
dc.contributor.author	Ali, Mohammed A.S.
dc.contributor.author	Fishman, Dmytro
dc.contributor.author	Parts, Leopold
dc.contributor.author	Grätz, Lukas
dc.contributor.author	Müller, Christoph
dc.contributor.author	Keller, Max
dc.contributor.author	Veiksina, Santa
dc.contributor.author	Laasfeld, Tõnis
dc.contributor.author	Rinken, Ago
dc.date.accessioned	2022-01-16T13:04:24Z
dc.date.available	2022-01-16T13:04:24Z
dc.date.issued	2022-01-13
dc.identifier.uri	https://datadoi.ee/handle/33/430
dc.identifier.uri	https://doi.org/10.23673/re-304
dc.description.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.	en
dc.format	ilp	en
dc.format	hdf5	en
dc.language.iso	en	en
dc.publisher	University of Tartu, Institute of Chemistry, Chair of Bioorganic chemistry	en
dc.rights	info:eu-repo/semantics/openAccess	en
dc.rights	Attribution 4.0 International	*
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	*
dc.subject	U-Net	en
dc.subject	Random forest	en
dc.subject	Cell segmentation	en
dc.subject	Ilastik	en
dc.title	UT-GPCR002 Machine learning models for CHO-K1 cell segmentation from fluorescence and bright-field microscopy images	en
dc.type	Model	en
dc.relation.iscitedby	https://doi.org/10.1101/2021.12.22.473643	en