Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network
- Submitting institution
-
University of Ulster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 78486125
- Type
- D - Journal article
- DOI
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10.1016/j.media.2019.06.007
- Title of journal
- Medical Image Analysis
- Article number
- -
- First page
- 1
- Volume
- 57
- Issue
- -
- ISSN
- 1361-8415
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- 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
-
9
- Research group(s)
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C - Pervasive Computing Research Centre
- Citation count
- 7
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- <23> The was part of deliverables to Horizon2020 DESIREE project (GrantNo. 690238, 02/2016-07/2019) and implemented in a prototype system clinically tested for adoption in Breast Units. The EU-appointed reviewer’s FinalReviewReport highlighted this work: “Some very relevant developments related to mammography image processing have been achieved with regard to image segmentation as well as breast density characterization. The integration of the Mammography segmentation in the Desiree platform is adequate, and provides a highly valuable tool in the diagnosis of dense breasts. These results are among the highest scored results during the clinical validation phase and they might have significant clinical impact.”
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -