Multi-scale Segmentation and Surface Fitting for Measuring 3D Macular Holes.
- Submitting institution
-
University of Durham
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 109004
- Type
- D - Journal article
- DOI
-
10.1109/tmi.2017.2767908
- Title of journal
- IEEE Transactions on Medical Imaging
- Article number
- -
- First page
- 580
- Volume
- 37
- Issue
- 2
- ISSN
- 02780062
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2017
- URL
-
https://doi.org/10.1109/tmi.2017.2767908
- 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
-
6
- Research group(s)
-
A - Innovative Computing
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- We introduce several novel techniques to automatically retrieve accurate 3-D measurements of the macular hole, including: surface area, base area, base diameter, top area, top diameter, height, and minimum diameter. Extensive statistical evaluation with a complete set of clinical measures evaluated and tested by ophthalmologists at the NHS Sunderland Eye Infirmary. IP in the paper used to establish Durham University spin-out, Intogral Limited (https://gtr.ukri.org/projects?ref=103594) - £201K R&D grant by Innovate UK, £60k investment by Northstar Ventures and a £25k accelerator grant by European Structure and Investment funds.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -