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Proceedings Paper

Context-based interpolation of coarse deep learning prediction maps for the segmentation of fine structures in immunofluorescence images
Author(s): Nicolas Brieu; Christos G. Gavriel; David J. Harrison; Peter D. Caie; Günter Schmidt
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Paper 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 extent are critical in this context. In this paper, we present a new approach based on visual context random forest to generate precise segmentation maps from deep learning coarse segmentation maps. Applied to the detection of cytokeratin positive (CK) epithelium regions in immunofluorescence (IF) images, we show that this method enables an accurate and fast detection of detailled 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 normalization algorithm for IF images is moreover introduced.

Paper Details

Date Published: 6 March 2018
PDF: 6 pages
Proc. SPIE 10581, Medical Imaging 2018: Digital Pathology, 105810P (6 March 2018); doi: 10.1117/12.2292794
Show Author Affiliations
Nicolas Brieu, Definiens AG (Germany)
Christos G. Gavriel, Univ. of St. Andrews (United Kingdom)
David J. Harrison, Univ. of St. Andrews (United Kingdom)
Peter D. Caie, Univ. of St. Andrews (United Kingdom)
Günter Schmidt, Definiens AG (Germany)


Published in SPIE Proceedings Vol. 10581:
Medical Imaging 2018: Digital Pathology
John E. Tomaszewski; Metin N. Gurcan, Editor(s)

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