Automated segment-level coronary artery calcium scoring on non-contrast CT: a multi-task deep-learning approach
Segment-level coronary artery calcium scoring is a tedious and error-prone task. The proposed multi-task model achieved good agreement with a human observer on the segment level. Deep learning can contribute to the automation of segment-level coronary artery calcium scoring.
Authors
- Wojciech Samek (ORCID: https://orcid.org/0000-0002-6283-3265)
- Maria Bosserdt (ORCID: https://orcid.org/0000-0002-6231-3999)
- Sebastian Stober (ORCID: https://orcid.org/0000-0002-1717-4133)
- Federico Biavati (ORCID: https://orcid.org/0000-0002-3558-3408)
- Bernhard Föllmer (ORCID: https://orcid.org/0000-0003-1377-3371)
- Klaus Fuglsang Kofoed (ORCID: https://orcid.org/0000-0001-9742-1554)
- Marc Dewey (ORCID: https://orcid.org/0000-0002-4402-2733)
- Kenrick Schulze (ORCID: https://orcid.org/0009-0006-7104-9049)
- Pál Maurovich‐Horvat (ORCID: https://orcid.org/0000-0003-0885-736X)
- Patrick Donnelly (ORCID: https://orcid.org/0000-0003-2797-8079)
- Theodora Benedek
- Michelle C. Williams
- Sotirios Tsogias
- Lars Gerrit Hövermann
Institutions
- Semmelweis University (HU)
- University of Copenhagen (DK)
- Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute (DE)
- Copenhagen University Hospital (DK)
- Rigshospitalet (DK)
- Universitatea de Medicină, Farmacie, Științe și Tehnologie „George Emil Palade” din Târgu Mureș (RO)
- Humboldt-Universität zu Berlin (DE)
- Berliner Institut für Sozialforschung (DE)
- German Centre for Cardiovascular Research (DE)
- South Eastern Health and Social Care Trust (GB)
- Berlin Institute for the Foundations of Learning and Data (DE)
- Technische Universität Berlin (DE)
- Freie Universität Berlin (DE)
- Charité - Universitätsmedizin Berlin (DE)
- Otto-von-Guericke-Universität Magdeburg (DE)
- University of Edinburgh (GB)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2024-10-16
- DOI
- https://doi.org/10.1186/s13244-024-01827-0
- Citations
- 8
- Primary Topic
- Advanced X-ray and CT Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.48
Funders
- European Commission
- Deutsche Forschungsgemeinschaft