Fast semi-automatic segmentation of focal liver lesions in contrast-enhanced ultrasound, based on a probabilistic model
- Submitting institution
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Kingston University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11-24-1357
- Type
- D - Journal article
- DOI
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10.1080/21681163.2015.1029642
- Title of journal
- Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization
- Article number
- -
- First page
- 329
- Volume
- 5
- Issue
- -
- ISSN
- 2168-1163
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2017
- URL
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- Supplementary information
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- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
-
- Research group(s)
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- Citation count
- 10
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Contrast-Enhanced Ultrasound (CEUS) is a fast new imaging technology that acquired FDA approval for the characterisation of liver lesions in 2016. However, automation of the process is difficult due to challenging nature of the captured CEUS videos. The research reported in this paper, conducted in collaborations with UK and Greek clinicians, is important, as it assists clinicians to segment liver lesions across multiple frames with minimal interaction, i.e., by only specifying a single seed point.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -