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dc.contributor.authorBrieu, Nicolas
dc.contributor.authorGavriel, Christos
dc.contributor.authorHarrison, David James
dc.contributor.authorCaie, Peter David
dc.contributor.authorSchmidt, Guenter
dc.contributor.editorTomaszewski, John E.
dc.contributor.editorGurcan, Metin N.
dc.date.accessioned2018-03-15T17:30:06Z
dc.date.available2018-03-15T17:30:06Z
dc.date.issued2018-03-06
dc.identifier.citationBrieu , N , Gavriel , C , Harrison , D J , Caie , P D & Schmidt , G 2018 , Context based interpolation of coarse deep learning prediction maps for the segmentation of fine structures in immunofluorescence images . in J E Tomaszewski & M N Gurcan (eds) , Medical Imaging 2018 : Digital Pathology . , 105810P , Proceedings of SPIE , vol. 10581 , SPIE , Symposium: SPIE Medical Imaging , Houston , United States , 10/02/18 . DOI: 10.1117/12.2292794en
dc.identifier.citationconferenceen
dc.identifier.issn0277-786X
dc.identifier.otherPURE: 252558377
dc.identifier.otherPURE UUID: e43dbf84-03d3-4284-a6b6-763218d2f1c5
dc.identifier.otherScopus: 85049198877
dc.identifier.urihttp://hdl.handle.net/10023/12955
dc.description.abstractThe automatic analysis of digital pathology images is becoming of increasing interest for the development of novel therapeutic drugs and of the associated companion diagnostic tests in oncology. A precise quantification of the tumor microenvironment and therefore an accurate segmentation of the tumor extend are critical in this context. In this paper, we present a new approach based on visual context Random Forest to generate high resolution segmentation maps from Deep Learning coarse segmentation maps. Through an example inimmunofluorescence, we show that this method enables an accurate and fast detection of the tumor structures in terms of qualitative and quantitative evaluation against three baseline approaches. For the method to be resilient to the high variability of staining intensity, a novel locally adaptive normalization algorithm is moreover introduced.en
dc.format.extent6en
dc.language.isoeng
dc.publisherSPIE
dc.relation.ispartofMedical Imaging 2018en
dc.relation.ispartofseriesProceedings of SPIEen
dc.rights© 2018, SPIE. This work has been made available online in accordance with the publisher’s policies. This is the final published version of the work, which was originally published at https://doi.org/10.1117/12.2292794en
dc.subjectDigital pathologyen
dc.subjectWhole slide imaging (WSI)en
dc.subjectImmunofluorescence (IF)en
dc.subjectDeep learningen
dc.subjectRandom Foresten
dc.subjectInterpolationen
dc.subjectSemantic segmentationen
dc.subjectRC0254 Neoplasms. Tumors. Oncology (including Cancer)en
dc.subjectT Technologyen
dc.subjectNDASen
dc.subject.lccRC0254en
dc.subject.lccTen
dc.titleContext based interpolation of coarse deep learning prediction maps for the segmentation of fine structures in immunofluorescence imagesen
dc.typeConference itemen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Medicineen
dc.contributor.institutionUniversity of St Andrews. Cellular Medicine Divisionen
dc.identifier.doihttps://doi.org/10.1117/12.2292794
dc.date.embargoedUntil06-03-20


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