Boundary Delineation of MRI Images for Lumbar Spinal Stenosis Detection through Semantic Segmentation using Deep Neural Networks
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 946
- Type
- D - Journal article
- DOI
-
10.1109/access.2019.2908002
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 43487
- Volume
- 7
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- April
- 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
-
9
- Research group(s)
-
-
- Citation count
- 7
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work is one of the three key outputs of an international research collaboration between LJMU and Universitas Multimedia Nusantara, Indonesia, funded by the Indonesian Ministry of Research, Technology and Higher Education through its competitive International Collaboration Programme (grant 034/KM/PNT/2018). For the first time, the result of boundary delineation of automatically segmented MRI images using SegNet is detailed to diagnose Lumbar Spinal Stenosis. The dataset used is much larger than those previously available to researchers and has been made freely available to the research community via Mendeley Data (DOI: 10.17632/k57fr854j2.2).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
