Deformable image registration by combining uncertainty estimates from supervoxel belief propagation
- Submitting institution
-
University of Sussex
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 504012_89512
- Type
- D - Journal article
- DOI
-
10.1016/j.media.2015.09.005
- Title of journal
- Medical Image Analysis
- Article number
- -
- First page
- 57
- Volume
- 27
- Issue
- -
- ISSN
- 1361-8415
- Open access status
- Out of scope for open access requirements
- Month of publication
- October
- Year of publication
- 2015
- URL
-
https://doi.org/10.1016/j.media.2015.09.005
- 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
-
4
- Research group(s)
-
-
- Citation count
- 35
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "This paper was significant in introducing a computationally efficient mechanism of accounting for correspondence uncertainty in high resolution 3D deformable registration. This was demonstrated to lead to accuracy improvements in multiple medical imaging domains. Modelling the uncertainty of deformable registration, and any derived imaging biomarkers, has downstream benefits in terms of use in clinical trials and monitoring changes in patient physiology. The work has field-weighted citation impact 4.12 (Scopus) and has been referred to by other leading groups in medical image analysis such as at MIT [1] and INRIA [2].
[1] https://doi.org/10.1016/j.media.2019.07.006
[2] https://doi.org/10.1016/j.media.2016.09.008"
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -