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dc.contributor.authorJuppet, Q
dc.contributor.authorDe Martino, F
dc.contributor.authorMarcandalli, E
dc.contributor.authorWeigert, M
dc.contributor.authorBurri, O
dc.contributor.authorUnser, M
dc.contributor.authorBrisken, C
dc.contributor.authorSage, D
dc.date.accessioned2021-08-05T09:51:24Z
dc.date.available2021-08-05T09:51:24Z
dc.identifier.citationJournal of mammary gland biology and neoplasia, 2021, 26 (2), pp. 101 - 112
dc.identifier.issn1083-3021
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/4724
dc.identifier.eissn1573-7039
dc.identifier.eissn1573-7039en_US
dc.identifier.doi10.1007/s10911-021-09485-4
dc.identifier.doi10.1007/s10911-021-09485-4en_US
dc.description.abstractPatient-Derived Xenografts (PDXs) are the preclinical models which best recapitulate inter- and intra-patient complexity of human breast malignancies, and are also emerging as useful tools to study the normal breast epithelium. However, data analysis generated with such models is often confounded by the presence of host cells and can give rise to data misinterpretation. For instance, it is important to discriminate between xenografted and host cells in histological sections prior to performing immunostainings. We developed Single Cell Classifier (SCC), a data-driven deep learning-based computational tool that provides an innovative approach for automated cell species discrimination based on a multi-step process entailing nuclei segmentation and single cell classification. We show that human and murine cell contextual features, more than cell-intrinsic ones, can be exploited to discriminate between cell species in both normal and malignant tissues, yielding up to 96% classification accuracy. SCC will facilitate the interpretation of H&E- and DAPI-stained histological sections of xenografted human-in-mouse tissues and it is open to new in-house built models for further applications. SCC is released as an open-source plugin in ImageJ/Fiji available at the following link: https://github.com/Biomedical-Imaging-Group/SingleCellClassifier .
dc.formatPrint-Electronic
dc.format.extent101 - 112
dc.languageeng
dc.language.isoeng
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleDeep Learning Enables Individual Xenograft Cell Classification in Histological Images by Analysis of Contextual Features.
dc.typeJournal Article
dcterms.dateAccepted2021-04-05
rioxxterms.versionVoR
rioxxterms.versionofrecord10.1007/s10911-021-09485-4
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0
dc.relation.isPartOfJournal of mammary gland biology and neoplasia
pubs.issue2
pubs.notesNot known
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Breast Cancer Research
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Breast Cancer Research/Endocrine control mechanisms
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Breast Cancer Research
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Breast Cancer Research/Endocrine control mechanisms
pubs.publication-statusPublished
pubs.volume26en_US
pubs.embargo.termsNot known
icr.researchteamEndocrine control mechanisms
icr.researchteamEndocrine control mechanismsen_US
dc.contributor.icrauthorBrisken, Cathrinen


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Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by/4.0/