Boundary Delineation of MRI Images for Lumbar Spinal Stenosis Detection Through Semantic Segmentation Using Deep Neural Networks
- Submitting institution
-
Teesside University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 16394675
- Type
- D - Journal article
- DOI
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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
- -
- 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
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9
- Research group(s)
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-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proposes a methodology to aid clinicians in performing lumbar spinal stenosis detection through semantic segmentation and delineation of magnetic resonance imaging (MRI) scans of the lumbar using deep learning. We worked with one specialty hospital in Jordan and several physicians and radiologists around the world to gather a significant number of relevant MRI scans complete with medical annotation to develop our dataset. We made this dataset freely available to the research community. This work was supported in part by the Indonesian Ministry of Research, Technology and Higher Education, under Grant 034/KM/PNT/2018.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -