Investigating the Impact of the Bit Depth of Fluorescence-Stained Images on the Performance of Deep Learning-Based Nuclei Instance Segmentation.
- DOI
- 10.3390/diagnostics11060967
- Published
- 2021 May 27
- Container
- Diagnostics (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/diagnostics11060967,
title = {Investigating the Impact of the Bit Depth of Fluorescence-Stained Images on the Performance of Deep Learning-Based Nuclei Instance Segmentation.},
author = {Mahbod A and Schaefer G and Löw C and Dorffner G and Ecker R and Ellinger I},
year = {2021},
journal = {Diagnostics (Basel, Switzerland)},
doi = {10.3390/diagnostics11060967},
url = {https://doi.org/10.3390/diagnostics11060967}
}RIS
TY - JOUR TI - Investigating the Impact of the Bit Depth of Fluorescence-Stained Images on the Performance of Deep Learning-Based Nuclei Instance Segmentation. AU - Mahbod A AU - Schaefer G AU - Löw C AU - Dorffner G AU - Ecker R AU - Ellinger I PY - 2021 JO - Diagnostics (Basel, Switzerland) DO - 10.3390/diagnostics11060967 UR - https://doi.org/10.3390/diagnostics11060967 ER -
APA
A, M., G, S., C, L., G, D., R, E., & I, E. (2021). Investigating the Impact of the Bit Depth of Fluorescence-Stained Images on the Performance of Deep Learning-Based Nuclei Instance Segmentation.. Diagnostics (Basel, Switzerland). https://doi.org/10.3390/diagnostics11060967
Source records
- pubmed · retrieved 2026-09-27T14:58:05.753Z