Workflow evaluation of a commercial deep learning-based image analysis tool for the quantification of angiogenesis in vivo.
- DOI
- 10.1038/s41598-026-68133-1
- Published
- 2026 Aug 28
- Container
- Scientific reports
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-026-68133-1,
title = {Workflow evaluation of a commercial deep learning-based image analysis tool for the quantification of angiogenesis in vivo.},
author = {Buhr CR and Maas AP and Wiesmann-Imilowski N and Brieger J and Matthias C and Ernst BP and Kloss-Brandstaetter A and Herrmann P and Eckrich J},
year = {2026},
journal = {Scientific reports},
doi = {10.1038/s41598-026-68133-1},
url = {https://doi.org/10.1038/s41598-026-68133-1}
}RIS
TY - JOUR TI - Workflow evaluation of a commercial deep learning-based image analysis tool for the quantification of angiogenesis in vivo. AU - Buhr CR AU - Maas AP AU - Wiesmann-Imilowski N AU - Brieger J AU - Matthias C AU - Ernst BP AU - Kloss-Brandstaetter A AU - Herrmann P AU - Eckrich J PY - 2026 JO - Scientific reports DO - 10.1038/s41598-026-68133-1 UR - https://doi.org/10.1038/s41598-026-68133-1 ER -
APA
CR, B., AP, M., N, W., J, B., C, M., BP, E., A, K., P, H., & J, E. (2026). Workflow evaluation of a commercial deep learning-based image analysis tool for the quantification of angiogenesis in vivo.. Scientific reports. https://doi.org/10.1038/s41598-026-68133-1
Source records
- pubmed · retrieved 2026-09-25T15:23:07.327Z