Investigating the Impact of the Bit Depth of Fluorescence-Stained Images on the Performance of Deep Learning-Based Nuclei Instance Segmentation

Investigating the Impact of the Bit Depth of Fluorescence-Stained Images on the Performance of Deep Learning-Based Nuclei Instance Segmentation

Nuclei instance segmentation can be considered as a key point in the computer-mediated analysis of histological fluorescence-stained (FS) images. Many computer-assisted approaches have been proposed for this task, and among them, supervised deep learning (DL) methods deliver the best performances. A...

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Bibliographic Details
Main Authors: Amirreza Mahbod, Gerald Schaefer, Christine Löw, Georg Dorffner, Rupert Ecker, Isabella Ellinger
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:Diagnostics
Subjects:
bit depth
nuclei segmentation
fluorescence staining
medical image analysis
computational pathology
deep learning
Online Access:https://www.mdpi.com/2075-4418/11/6/967

Internet

https://www.mdpi.com/2075-4418/11/6/967

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