KEVS: enhancing segmentation of visceral adipose tissue in pre-cystectomy CT with Gaussian kernel density estimation
This research introduces KEVS, an automated, SOTA method for the prediction of VAT in pre-cystectomy CT which eliminates inter-observer variability and is trained entirely on open-source CT datasets which do not contain ground-truth VAT masks.
Authors
- Sven Kerneis (ORCID: https://orcid.org/0000-0002-9683-2295)
- Nicholas Tetlow (ORCID: https://orcid.org/0000-0002-4907-9018)
- Annie Fung (ORCID: https://orcid.org/0000-0001-6688-904X)
- Evangelos B. Mazomenos (ORCID: https://orcid.org/0000-0003-0357-5996)
- Amy Dewar (ORCID: https://orcid.org/0009-0007-3015-7040)
- Pietro Arina (ORCID: https://orcid.org/0000-0002-2080-5274)
- John Whittle (ORCID: https://orcid.org/0000-0002-3859-679X)
- Thomas Boucher (ORCID: https://orcid.org/0009-0002-6152-7271)
Institutions
- University College London Hospitals NHS Foundation Trust (GB)
- Society of Interventional Radiology (US)
- UCL Biomedical Research Centre (GB)
- Centre for Clinical Interventions (AU)
- The London College (GB)
- University College London (GB)
Publication Details
- Journal
- International Journal of Computer Assisted Radiology and Surgery
- Published
- 2025-05-09
- DOI
- https://doi.org/10.1007/s11548-025-03380-7
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- International Anesthesia Research Society
- UCLH Biomedical Research Centre