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MAJERCIK, J. SPACEK, M.
Originální název
CELL AND SUB-CELLULAR SEGMENTATION IN QUANTITATIVE PHASE IMAGING USING U-NET
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
Theabilitytoautomaticallysegmentimages,especiallymicroscopyimagesofcells,opens newopportunitiesincancerresearchorotherpracticalapplications.Recentadvancementsindeep learningenabledforeffectivesingle-cellsegmentation,however,automaticsegmentationofsubcellularregionsisstillchallenging.ThisworkdescribesanimplementationofaU-netneuralnetworkforlabel-freesegmentationofsub-cellularregionsonimagesofadherentprostatecancercells, specificallyPC-3and22Rv1.Usingthebestperformingapproach,outofallthathavebeentested, wehavemanagedtodistinguishbetweenobjectsandbackgroundwithaveragedicecoefficientsof 0.83,0.78and0.63forwholecells,nucleiandnucleolirespectively.
Klíčová slova
cellsegmentation,deeplearning,neuralnetwork,quantitativephaseimaging,nucleus, nucleolus
Autoři
MAJERCIK, J.; SPACEK, M.
Vydáno
27. 4. 2021
Nakladatel
Brno University of Technology, Faculty of Electrical Engineering and Communication
Místo
Brno
ISBN
978-80-214-5943-4
Kniha
PROCEEDINGS II OF THE 27TH STUDENT EEICT 2021 selected papers
Edice
1
Strany od
9
Strany do
12
Strany počet
4
URL
https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2021_sbornik_2_v3_DOI.pdf
BibTex
@inproceedings{BUT183885, author="Jakub {Majerčík} and Michal {Špaček}", title="CELL AND SUB-CELLULAR SEGMENTATION IN QUANTITATIVE PHASE IMAGING USING U-NET", booktitle="PROCEEDINGS II OF THE 27TH STUDENT EEICT 2021 selected papers", year="2021", series="1", pages="9--12", publisher="Brno University of Technology, Faculty of Electrical Engineering and Communication", address="Brno", doi="10.13164/eeict.2021.9", isbn="978-80-214-5943-4", url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2021_sbornik_2_v3_DOI.pdf" }