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

DOI

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.

Identifier
DOI https://datadoi.ee/handle/33/430
Related Identifier https://doi.org/10.1101/2021.12.22.473643
Metadata Access https://datadoi.ee/oai/request?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai:datadoi.ee:33/430
Provenance
Creator 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
Publisher University of Tartu, Institute of Chemistry, Chair of Bioorganic chemistry
Publication Year 2022
Rights info:eu-repo/semantics/openAccess; Attribution 4.0 International; http://creativecommons.org/licenses/by/4.0/
OpenAccess true
Contact University of Tartu, Institute of Chemistry, Chair of Bioorganic chemistry
Representation
Language English
Resource Type Model
Format ilp; hdf5; application/zip; text/plain
Discipline Other