A Comparative Study of Cell Nuclei Attributed Relational Graphs for Knowledge Description and Categorization in Histopathological Gastric Cancer Whole Slide Images

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-22 DOI:10.1109/CBMS.2017.25

H. Sharma, N. Zerbe, Christin Boger, S. Wienert, O. Hellwich, P. Hufnagl

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引用次数: 13

Abstract

In this paper, cell nuclei attributed relational graphs are extensively studied and comparatively analyzed for effective knowledge description and classification in H&E stained whole slide images of gastric cancer. This includes design and implementation of multiple graph variations with diverse tissue component characteristics and architectural properties to obtain enhanced image representations, followed by hierarchical ensemble learning and classification. A detailed comparative analysis of the proposed graph-based methods, also with the established low-level, object-level and high-level image descriptions is performed, that further leads to a hybrid approach combining salient visual information. Quantitative evaluation of investigated methods suggests the suitability of particular graph variants for automatic classification using H&E stained histopathological gastric cancer whole slide images based on HER2 immunohistochemistry.

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组织病理学胃癌全片图像中细胞核归属关系图知识描述与分类的比较研究

本文对胃癌H&E染色整张切片图像的细胞核归属关系图进行了广泛的研究和对比分析，以便对其进行有效的知识描述和分类。这包括设计和实现具有不同组织成分特征和建筑属性的多个图形变体，以获得增强的图像表示，然后是分层集成学习和分类。对所提出的基于图的方法进行了详细的比较分析，并与已建立的低级，对象级和高级图像描述进行了比较分析，从而进一步导致结合显著视觉信息的混合方法。对所研究方法的定量评价表明，基于HER2免疫组化的H&E染色的组织病理学胃癌全切片图像，特定的图变体适合于自动分类。

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2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)

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