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Research at St Andrews

Context based interpolation of coarse deep learning prediction maps for the segmentation of fine structures in immunofluorescence images

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Author(s)

Nicolas Brieu, Christos Gavriel, David James Harrison, Peter David Caie, Guenter Schmidt

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Abstract

The 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.
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Details

Original languageEnglish
Title of host publicationMedical Imaging 2018
Subtitle of host publicationDigital Pathology
EditorsJohn E. Tomaszewski, Metin N. Gurcan
PublisherSPIE
Number of pages6
DOIs
StatePublished - 6 Mar 2018
EventSymposium: SPIE Medical Imaging : Digital Pathology - Marriott Marquis Houston, Houston, United States
Duration: 10 Feb 201815 Feb 2018
Conference number: 10581
https://spie.org/MI/conferencedetails/digital-pathology

Publication series

NameProceedings of SPIE
PublisherSociety of Photo-optical Instrumentation Engineers
Volume10581
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceSymposium: SPIE Medical Imaging
CountryUnited States
CityHouston
Period10/02/1815/02/18
Internet address

    Research areas

  • Digital pathology, Whole slide imaging (WSI), Immunofluorescence (IF), Deep learning, Random Forest, Interpolation, Semantic segmentation

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ID: 252558377