Mahbod, A.; Schaefer, G.; Löw, C.; Dorffner, G.; Ecker, R.; Ellinger, I.
Investigating the Impact of the Bit Depth of Fluorescence-Stained Images on the Performance of Deep Learning-Based Nuclei Instance Segmentation. Diagnostics 2021, 11, 967.
https://doi.org/10.3390/diagnostics11060967
AMA Style
Mahbod A, Schaefer G, Löw C, Dorffner G, Ecker R, Ellinger I.
Investigating the Impact of the Bit Depth of Fluorescence-Stained Images on the Performance of Deep Learning-Based Nuclei Instance Segmentation. Diagnostics. 2021; 11(6):967.
https://doi.org/10.3390/diagnostics11060967
Chicago/Turabian Style
Mahbod, Amirreza, Gerald Schaefer, Christine Löw, Georg Dorffner, Rupert Ecker, and Isabella Ellinger.
2021. "Investigating the Impact of the Bit Depth of Fluorescence-Stained Images on the Performance of Deep Learning-Based Nuclei Instance Segmentation" Diagnostics 11, no. 6: 967.
https://doi.org/10.3390/diagnostics11060967
APA Style
Mahbod, A., Schaefer, G., Löw, C., Dorffner, G., Ecker, R., & Ellinger, I.
(2021). Investigating the Impact of the Bit Depth of Fluorescence-Stained Images on the Performance of Deep Learning-Based Nuclei Instance Segmentation. Diagnostics, 11(6), 967.
https://doi.org/10.3390/diagnostics11060967
