Deep learning for opportunistic, end-to-end automated assessment of epicardial adipose tissue in pre-interventional, ECG-gated spiral computed tomography
Deep learning methods for automatic assessment of epicardial adipose tissue (EAT) have great potential. A 2-step approach with slice extraction and tissue segmentation enables robust automated evaluation of EAT. End-to-end automation enables large-scale research on the value of EAT for outcome analysis.
Authors
- Maike Theis (ORCID: https://orcid.org/0000-0003-2666-9928)
- Daniel Kuetting (ORCID: https://orcid.org/0000-0001-6520-1020)
- Julian Alexander Luetkens (ORCID: https://orcid.org/0000-0002-7715-4636)
- Wolfgang Block (ORCID: https://orcid.org/0000-0003-3095-6356)
- Ulrike Attenberger (ORCID: https://orcid.org/0000-0002-8123-5447)
- Sebastian Nowak (ORCID: https://orcid.org/0000-0001-5175-5559)
- Alois M. Sprinkart (ORCID: https://orcid.org/0000-0002-1435-9562)
- Laura Garajová
- Babak Salam
Institutions
- University Hospital Bonn (DE)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2024-12-19
- DOI
- https://doi.org/10.1186/s13244-024-01875-6
- Citations
- 1
- Primary Topic
- Cardiovascular Disease and Adiposity
- Type
- article
- Field-Weighted Citation Impact
- 0.40
Funders
- Bundesministerium für Bildung und Forschung