Deep learning for automated boundary detection and segmentation in organ donation photography
Accurate, rapid and automated image segmentation in the context of surgical photography is possible with open-source deep-learning software. These outperform existing methods and could impact the field of surgery, enabling similar advancements seen in other areas of medical computer vision.
Authors
- Beth Gibson (ORCID: https://orcid.org/0000-0002-3782-1975)
- Neil Sheerin (ORCID: https://orcid.org/0000-0002-3743-2371)
- James Hunter (ORCID: https://orcid.org/0000-0002-4972-6879)
- Balaji Mahendran (ORCID: https://orcid.org/0000-0002-6252-9415)
- Ali Ahmed Elmahmudi
- Emily K. Glover (ORCID: https://orcid.org/0009-0008-0704-8601)
- Chloe Connelly (ORCID: https://orcid.org/0009-0008-1425-9095)
- Colin Wilson (ORCID: https://orcid.org/0000-0002-1515-8690)
- Brian Thomson
- Samuel J Tingle (ORCID: https://orcid.org/0000-0001-5529-7815)
- Hassan Ugail (ORCID: https://orcid.org/0000-0002-3084-1797)
- Lucy Bates (ORCID: https://orcid.org/0000-0002-6113-6388)
- Georgios Kourounis (ORCID: https://orcid.org/0000-0002-1051-078X)
- Emily Thompson (ORCID: https://orcid.org/0000-0002-7449-6294)
- Robin Nandi
Institutions
- NHS Blood and Transplant (GB)
- University of Bradford (GB)
- Freeman Hospital (GB)
- University of Oxford (GB)
- Newcastle University (GB)
Publication Details
- Journal
- Innovative Surgical Sciences
- Published
- 2024-08-19
- DOI
- https://doi.org/10.1515/iss-2024-0022
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.81
Funders
- NHS Blood and Transplant
- Wellcome Trust
- National Institute for Health and Care Research
- Department of Health and Social Care
- Newcastle University
- Northern Counties Kidney Research Fund