A Deep Learning-Enabled Toolkit for the 3D Segmentation of Ventricular Cardiomyocytes
Segmentation of cardiomyocytes in microscopic 3D volumes is key to our understanding of cardiac (patho-)physiology; however, it poses substantial experimental and analytical challenges. Therefore, researchers often resort to inferring 3D information f
Authors
- Thomas Seidel (ORCID: https://orcid.org/0000-0002-3127-1693)
- Josef Madl (ORCID: https://orcid.org/0000-0003-3637-2546)
- Peter Karl Kohl (ORCID: https://orcid.org/0000-0003-0416-6270)
- Eva A. Rog‐Zielinska
- Joachim Greiner (ORCID: https://orcid.org/0000-0001-5454-2962)
- Fabio Frangiamore
- Frédéric Sonak (ORCID: https://orcid.org/0009-0008-0241-0894)
Institutions
- University of Freiburg (DE)
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
Publication Details
- Published
- 2025-03-24
- DOI
- https://doi.org/10.22541/au.173697566.64378528/v2
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00