Yang, S. orcid.org/0000-0003-0531-2903, Lemke, C. orcid.org/0000-0003-4003-4814, Cox, B.F. et al. (3 more authors) (2020) A Learning-Based Microultrasound System for the Detection of Inflammation of the Gastrointestinal Tract. IEEE Transactions on Medical Imaging, 40 (1). pp. 38-47. ISSN 0278-0062
Abstract
Inflammation of the gastrointestinal (GI) tract accompanies several diseases, including Crohn's disease. Currently, video capsule endoscopy and deep bowel enteroscopy are the main means for direct visualisation of the bowel surface. However, the use of optical imaging limits visualisation to the luminal surface only, which makes earlystage diagnosis difficult. In this study, we propose a learning enabled microultrasound (μUS) system that aims to classify inflamed and non-inflamedbowel tissues. μUS images of the caecum, small bowel and colon were obtained from mice treated with agents to induce inflammation. Those images were then used to train three deep learning networks and to provide a ground truth of inflammation status. The classification accuracy was evaluated using 10-fold evaluation and additional B-scan images. Our deep learning approach allowed robust differentiation between healthy tissue and tissue with early signs of inflammation that is not detectable by current endoscopic methods or by human inspection of the μUS images. The methods may be a foundation for future early GI disease diagnosis and enhanced management with computer-aided imaging.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Keywords: | Computer-aided detection and diagnosis, gastrointestinal tract, ultrasound, neural network |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Medical and Biological Engineering (iMBE) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 02 Feb 2024 10:59 |
Last Modified: | 02 Feb 2024 10:59 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Identification Number: | 10.1109/tmi.2020.3021560 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:208590 |