Workflow evaluation of a commercial deep learning-based image analysis tool for the quantification of angiogenesis in vivo.

Buhr CR, Maas AP, Wiesmann-Imilowski N, Brieger J, Matthias C, Ernst BP, Kloss-Brandstaetter A, Herrmann P, Eckrich J

Open source

DOI
10.1038/s41598-026-68133-1
Published
2026 Aug 28
Container
Scientific reports
Publisher
Not recorded
Open access
yes

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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

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