Spatial statistical modelling of capillary non-perfusion in the retina
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1052
- Type
- D - Journal article
- DOI
-
10.1038/s41598-017-16620-x
- Title of journal
- SCIENTIFIC REPORTS
- Article number
- ARTN 16792
- First page
- -
- Volume
- 7
- Issue
- 1
- ISSN
- 2045-2322
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2017
- 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
-
6
- Research group(s)
-
-
- Citation count
- 4
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper is the first to analyse Capillary Non-Perfusion (CNP) using spatial statistical modelling. CNP is an important feature of several common retinal diseases and a key outcome variable for randomised clinical trials. Spatial distribution is important because it is likely to contain information about regional variation in microvascular anatomy and physiology within the macula. This paper contributed to setting the agenda for reliable and interpretable CNP segmentation data, including an invitation as a Plenary Speaker at the APTOS2019 symposium. It also led to PhD studentship funding from the BCPB.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -