Cell nuclei attributed relational graphs for efficient representation and classification of gastric cancer in digital histopathology
| Autor: | Norman Zerbe, Stephan Wienert, Daniel Heim, Harshita Sharma, Peter Hufnagl, Olaf Hellwich, Sebastian Lohmann |
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| Rok vydání: | 2016 |
| Předmět: |
Vertex (graph theory)
medicine.medical_specialty Computer science Local binary patterns 02 engineering and technology Gastric carcinoma Haematoxylin Malignancy Stain 03 medical and health sciences chemistry.chemical_compound 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering medicine Computer vision Eosin business.industry Delaunay triangulation Cancer Digital pathology Pattern recognition Image segmentation medicine.disease chemistry 030220 oncology & carcinogenesis Immunohistochemistry 020201 artificial intelligence & image processing Histopathology Artificial intelligence business Voronoi diagram |
| Zdroj: | Medical Imaging: Digital Pathology |
| ISSN: | 0277-786X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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