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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
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WRAP-Cellular-community-detection-tissue-phenotyping-colorectal-Rajpoot-2020.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (44Mb) | Preview |
Official URL: https://doi.org/10.1016/j.media.2020.101696
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 | ||||||||
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| Subjects: | Q Science > QH Natural history R Medicine > RB Pathology R Medicine > RC Internal medicine |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science Faculty of Science, Engineering and Medicine > Science > Mathematics |
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| 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: |
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| 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 |
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