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Cellular community detection for tissue phenotyping in colorectal cancer histology images

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Javed, Sajid, Mahmood, Arif, Fraz, Muhammad Moazam, Koohbanani, Navid Alemi, Benes, Ksenija, Tsang, Yee-Wah, Hewitt, Katherine J., Epstein, D. B. A., Snead, David and Rajpoot, Nasir M. (Nasir Mahmood) (2020) Cellular community detection for tissue phenotyping in colorectal cancer histology images. Medical Image Analysis, 63 . 101696. doi:10.1016/j.media.2020.101696 ISSN 13618415.

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Official URL: https://doi.org/10.1016/j.media.2020.101696

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Abstract

Classification of various types of tissue in cancer histology images based on the cellular compositions is an important step towards the development of computational pathology tools for systematic digital profiling of the spatial tumor microenvironment. Most existing methods for tissue phenotyping are limited to the classification of tumor and stroma and require large amount of annotated histology images which are often not available. In the current work, we pose the problem of identifying distinct tissue phenotypes as finding communities in cellular graphs or networks. First, we train a deep neural network for cell detection and classification into five distinct cellular components. Considering the detected nuclei as nodes, potential cell-cell connections are assigned using Delaunay triangulation resulting in a cell-level graph. Based on this cell graph, a feature vector capturing potential cell-cell connection of different types of cells is computed. These feature vectors are used to construct a patch-level graph based on chi-square distance. We map patch-level nodes to the geometric space by representing each node as a vector of geodesic distances from other nodes in the network and iteratively drifting the patch nodes in the direction of positive density gradients towards maximum density regions. The proposed algorithm is evaluated on a publicly available dataset and another new large-scale dataset consisting of 280K patches of seven tissue phenotypes. The estimated communities have significant biological meanings as verified by the expert pathologists. A comparison with current state-of-the-art methods reveals significant performance improvement in tissue phenotyping.

Item Type: Journal Article
Subjects: Q Science > QH Natural history
R Medicine > RB Pathology
R Medicine > RC Internal medicine
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Faculty of Science, Engineering and Medicine > Science > Mathematics
SWORD Depositor: Library Publications Router
Library of Congress Subject Headings (LCSH): Pathology -- Digital techniques, Phenotype, Tumors -- Molecular aspects, Diagnostic imaging, Diagnostic imaging -- Digital techniques, Colon (Anatomy) -- Cancer -- Imaging
Journal or Publication Title: Medical Image Analysis
Publisher: Elsevier
ISSN: 13618415
Official Date: July 2020
Dates:
DateEvent
July 2020Published
13 April 2020Available
2 April 2020Accepted
Volume: 63
Article Number: 101696
DOI: 10.1016/j.media.2020.101696
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 20 April 2020
Date of first compliant Open Access: 13 April 2021

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