Automated cardiovascular magnetic resonance image analysis with fully convolutional networks
- Submitting institution
-
Imperial College of Science, Technology and Medicine
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2154
- Type
- D - Journal article
- DOI
-
10.1186/s12968-018-0471-x
- Title of journal
- Journal of Cardiovascular Magnetic Resonance
- Article number
- 65
- First page
- 1
- Volume
- 20
- Issue
- 1
- ISSN
- 1097-6647
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
10.1186/s12968-018-0471-x
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
22
- Research group(s)
-
-
- Citation count
- 131
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- First ever deep learning segmentation algorithm for cardiac MR imaging that has been trained and evaluated using 100,000 images from over 4,500 patients. Winner of Best Paper Award at JCMR 2019 (Gerald M. Pohost Award, https://scmr.org/page/2019AwardRecipients). Adopted by several companies (e.g. https://www.circlecvi.com/cvi42/cardiac-mri/deep-learning/). The work led to Rueckert's appointment as consultant to Circle CVI (https://www.circlecvi.com/).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
