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Proceedings Paper

Deciphering protein signatures using color, morphological, and topological analysis of immunohistochemically stained human tissues
Author(s): Erwan Zerhouni; Bogdan Prisacari; Qing Zhong; Peter Wild; Maria Gabrani
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Paper Abstract

Images of tissue specimens enable evidence-based study of disease susceptibility and stratification. Moreover, staining technologies empower the evidencing of molecular expression patterns by multicolor visualization, thus enabling personalized disease treatment and prevention. However, translating molecular expression imaging into direct health benefits has been slow. Two major factors contribute to that. On the one hand, disease susceptibility and progression is a complex, multifactorial molecular process. Diseases, such as cancer, exhibit cellular heterogeneity, impeding the differentiation between diverse grades or types of cell formations. On the other hand, the relative quantification of the stained tissue selected features is ambiguous, tedious and time consuming, prone to clerical error, leading to intra- and inter-observer variability and low throughput. Image analysis of digital histopathology images is a fast-developing and exciting area of disease research that aims to address the above limitations. We have developed a computational framework that extracts unique signatures using color, morphological and topological information and allows the combination thereof. The integration of the above information enables diagnosis of disease with AUC as high as 0.97. Multiple staining show significant improvement with respect to most proteins, and an AUC as high as 0.99.

Paper Details

Date Published: 23 March 2016
PDF: 10 pages
Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 97910T (23 March 2016); doi: 10.1117/12.2218016
Show Author Affiliations
Erwan Zerhouni, IBM Research - Zürich (Switzerland)
Bogdan Prisacari, IBM Research - Zürich (Switzerland)
Qing Zhong, UniversitätsSpital Zürich (Switzerland)
Peter Wild, UniversitätsSpital Zürich (Switzerland)
Maria Gabrani, IBM Research - Zürich (Switzerland)

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

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