Tensor-based computation and modeling in multi-resolution digital pathology imaging: application to follicular lymphoma grading

Evrim Acar; Gerard Lozanski; Metin N. Gurcan

doi:10.1117/12.2006025

29 March 2013 Tensor-based computation and modeling in multi-resolution digital pathology imaging: application to follicular lymphoma grading

Evrim Acar, Gerard Lozanski, Metin N. Gurcan

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Proceedings Volume 8676, Medical Imaging 2013: Digital Pathology; 867603 (2013) https://doi.org/10.1117/12.2006025
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

In this work, we introduce a tensor-based computation and modeling framework for the analysis of digital pathology images at different resolutions. We represent digital pathology images as a third-order tensor (a three-way array) with modes: images, features and scales, by extracting features at different scales. The constructed tensor is then analyzed using the most popular tensor factorization methods, i.e., CANDECOMP/PARAFAC and Tucker. These tensor models enable us to extract the underlying patterns in each mode (i.e. images, features and scales) and examine how these patterns are related to each other. As a motivating example, we analyzed 500 follicular lymphoma images corresponding to high power fields, evaluated by three expert hematopathologists. Numerical experiments demonstrate that (i) tensor models capture easily-interpretable patterns showing the significant features and scales, and (ii) patterns extracted by the right tensor model, which in this case is the Tucker model commonly used for exploratory analysis of higher-order tensors, perform as well as the reduced dimensions captured by matrix factorization methods on unfolded data, in terms of follicular lymphoma grading.

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Evrim Acar, Gerard Lozanski, and Metin N. Gurcan "Tensor-based computation and modeling in multi-resolution digital pathology imaging: application to follicular lymphoma grading", Proc. SPIE 8676, Medical Imaging 2013: Digital Pathology, 867603 (29 March 2013); https://doi.org/10.1117/12.2006025

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KEYWORDS

Data modeling

Lymphoma

Feature extraction

Pathology

Image resolution

Performance modeling

Algorithm development

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