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Proceedings Paper

Cell nuclei attributed relational graphs for efficient representation and classification of gastric cancer in digital histopathology
Author(s): Harshita Sharma; Norman Zerbe; Daniel Heim; Stephan Wienert; Sebastian Lohmann; Olaf Hellwich; Peter Hufnagl
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Paper Abstract

This paper describes a novel graph-based method for efficient representation and subsequent classification in histological whole slide images of gastric cancer. Her2/neu immunohistochemically stained and haematoxylin and eosin stained histological sections of gastric carcinoma are digitized. Immunohistochemical staining is used in practice by pathologists to determine extent of malignancy, however, it is laborious to visually discriminate the corresponding malignancy levels in the more commonly used haematoxylin and eosin stain, and this study attempts to solve this problem using a computer-based method. Cell nuclei are first isolated at high magnification using an automatic cell nuclei segmentation strategy, followed by construction of cell nuclei attributed relational graphs of the tissue regions. These graphs represent tissue architecture comprehensively, as they contain information about cell nuclei morphology as vertex attributes, along with knowledge of neighborhood in the form of edge linking and edge attributes. Global graph characteristics are derived and ensemble learning is used to discriminate between three types of malignancy levels, namely, non-tumor, Her2/neu positive tumor and Her2/neu negative tumor. Performance is compared with state of the art methods including four texture feature groups (Haralick, Gabor, Local Binary Patterns and Varma Zisserman features), color and intensity features, and Voronoi diagram and Delaunay triangulation. Texture, color and intensity information is also combined with graph-based knowledge, followed by correlation analysis. Quantitative assessment is performed using two cross validation strategies. On investigating the experimental results, it can be concluded that the proposed method provides a promising way for computer-based analysis of histopathological images of gastric cancer.

Paper Details

Date Published: 23 March 2016
PDF: 19 pages
Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 97910X (23 March 2016); doi: 10.1117/12.2216843
Show Author Affiliations
Harshita Sharma, Technische Univ. Berlin (Germany)
Norman Zerbe, Charité Univ. Hospital Berlin (Germany)
Daniel Heim, Charité Univ. Hospital Berlin (Germany)
Stephan Wienert, Charité Univ. Hospital Berlin (Germany)
Sebastian Lohmann, Charité Univ. Hospital Berlin (Germany)
Olaf Hellwich, Technische Univ. Berlin (Germany)
Peter Hufnagl, Charité Univ. Hospital Berlin (Germany)

Published in SPIE Proceedings Vol. 9791:
Medical Imaging 2016: Digital Pathology
Metin N. Gurcan; Anant Madabhushi, Editor(s)

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