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DC poleHodnotaJazyk
dc.contributor.authorMatuszewski, Damian J.
dc.contributor.authorRanefall, Peter
dc.contributor.editorSkala, Václav
dc.date.accessioned2021-09-01T09:06:23Z
dc.date.available2021-09-01T09:06:23Z
dc.date.issued2021
dc.identifier.citationWSCG 2021: full papers proceedings: 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 335-338.en
dc.identifier.isbn978-80-86943-34-3
dc.identifier.issn2464-4617
dc.identifier.issn2464–4625(CD/DVD)
dc.identifier.urihttp://hdl.handle.net/11025/45040
dc.format4 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.rights© Václav Skala - UNION Agencycs
dc.subjecthluboké učenícs
dc.subjectU-Netcs
dc.subjectCellProfilercs
dc.subjectanotace datcs
dc.subjectmikroskopiecs
dc.titleLearning Cell Nuclei Segmentation Using Labels Generated With Classical Image Analysis Methodsen
dc.typeconferenceObjecten
dc.typekonferenční příspěvekcs
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedCreating manual annotations in a large number of images is a tedious bottleneck that limits deep learning use in many applications. Here, we present a study in which we used the output of a classical image analysis pipelineas labels when training a convolutional neural network(CNN). This may not only reduce the time experts spend annotating images but it may also lead to an improvement of results when compared to the output from the classical pipeline used in training. Inour application, i.e.,cell nuclei segmentation,we generated the annotations using CellProfiler(a tool for developing classical image analysis pipelines for biomedical applications)and trained on them a U-Net-based CNN model. The best model achieved a 0.96 dice-coefficient of the segmented Nuclei and a 0.84 object-wise Jaccard indexwhich was better than the classical method used for generating the annotations by 0.02and 0.34, respectively. Our experimental results show that in this application, not only such training is feasiblebut also thatthe deep learning segmentationsare a clear improvement compared to the output from the classical pipelineused for generating the annotations.en
dc.subject.translateddeep learningen
dc.subject.translatedU-Neten
dc.subject.translatedCellProfileren
dc.subject.translateddata annotationen
dc.subject.translatedmicroscopyen
dc.identifier.doihttps://doi.org/10.24132/CSRN.2021.3101.37
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:WSCG 2021: Full Papers Proceedings

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