A stochastic polygons model for glandular structures in colon histology images
- Submitting institution
-
The University of Warwick
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 6088
- Type
- D - Journal article
- DOI
-
10.1109/TMI.2015.2433900
- Title of journal
- IEEE Transactions on Medical Imaging
- Article number
- -
- First page
- 2366
- Volume
- 34
- Issue
- 11
- ISSN
- 0278-0062
- Open access status
- Out of scope for open access requirements
- Month of publication
- November
- Year of publication
- 2015
- 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
-
2
- Research group(s)
-
A - Applied Computing
- Citation count
- 72
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This highly cited work, funded by a $1M Qatar Foundation grant and published in a top journal in the field, proposed the first Bayesian inference method for segmentation of glandular structures in colorectal cancer (CRC) histology images. It provides a stochastic framework for fast gland segmentation in poorly differentiated (high grade) CRC histology images. The GlaS dataset released with this article has served as the international benchmark for gland segmentation, further advancing the field. This research has been granted a US Patent (US8,712,142), and it was one of the two papers that formed the basis of an MRC award (MR/P015476/1).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -