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Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features
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Qaiser, Talha, Tsang, Yee-Wah, Taniyama, Daiki, Sakamoto, Naoya, Nakane, Kazuaki, Epstein, D. B. A. and Rajpoot, Nasir M. (Nasir Mahmood) (2019) Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features. Medical Image Analysis, 55 . pp. 1-14. doi:10.1016/j.media.2019.03.014 ISSN 1361-8415.
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WRAP-fast-accurate-tumor-segmentation-histololgy-images-Rajpoot-2019.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (36Mb) | Preview |
Official URL: https://doi.org/10.1016/j.media.2019.03.014
Abstract
Tumor segmentation in whole-slide images of histology slides is an important step towards computer-assisted diagnosis. In this work, we propose a tumor segmentation framework based on the novel concept of persistent homology profiles (PHPs). For a given image patch, the homology profiles are derived by efficient computation of persistent homology, which is an algebraic tool from homology theory. We propose an efficient way of computing topological persistence of an image, alternative to simplicial homology. The PHPs are devised to distinguish tumor regions from their normal counterparts by modeling the atypical characteristics of tumor nuclei. We propose two variants of our method for tumor segmentation: one that targets speed without compromising accuracy and the other that targets higher accuracy. The fast version is based on the selection of exemplar image patches from a convolution neural network (CNN) and patch classification by quantifying the divergence between the PHPs of exemplars and the input image patch. Detailed comparative evaluation shows that the proposed algorithm is significantly faster than competing algorithms while achieving comparable results. The accurate version combines the PHPs and high-level CNN features and employs a multi-stage ensemble strategy for image patch labeling. Experimental results demonstrate that the combination of PHPs and CNN features outperforms competing algorithms. This study is performed on two independently collected colorectal datasets containing adenoma, adenocarcinoma, signet and healthy cases. Collectively, the accurate tumor segmentation produces the highest average patch-level F1-score, as compared with competing algorithms, on malignant and healthy cases from both the datasets. Overall the proposed framework highlights the utility of persistent homology for histopathology image analysis.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > QA Mathematics R Medicine > RB Pathology |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||
Library of Congress Subject Headings (LCSH): | Histology, Pathological, Homology theory -- Industrial applications | ||||||||
Journal or Publication Title: | Medical Image Analysis | ||||||||
Publisher: | Elsevier Science BV | ||||||||
ISSN: | 1361-8415 | ||||||||
Official Date: | July 2019 | ||||||||
Dates: |
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Volume: | 55 | ||||||||
Page Range: | pp. 1-14 | ||||||||
DOI: | 10.1016/j.media.2019.03.014 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 1 April 2019 | ||||||||
Date of first compliant Open Access: | 4 April 2020 | ||||||||
Related URLs: | |||||||||
Open Access Version: |
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