Embedding MRI information into MRSI data source extraction improves brain tumour delineation in animal models
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 962
- Type
- D - Journal article
- DOI
-
10.1371/journal.pone.0220809
- Title of journal
- PLoS ONE
- Article number
- e0220809
- First page
- -
- Volume
- 14
- Issue
- 8
- ISSN
- 1932-6203
- Open access status
- Compliant
- Month of publication
- August
- 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
- Yes
- Number of additional authors
-
5
- Research group(s)
-
-
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This study proposes a new methodology, Semi-Supervised Source Extraction (SSSE), which advances the state-of-the-art in both methodological and application fields, by guiding the extraction of relevant spectral sources with embedded morphological information. SSSE successfully improves brain tumour delineation whilst introducing novel benefits: 1) Not constricting the metabolomic-based prediction to the image-segmented area; 2) the ability to deal with signal-to-noise issues; 3) an opportunity to answer specific clinical questions; 4) the facilitation of intra-subject analysis; and 5) the extraction of meaningful, interpretable, good-quality sources.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
