Normalization of HE-stained histological images using cycle consistent generative adversarial networks

Normalization of HE-stained histological images using cycle consistent generative adversarial networks

Abstract Background Histological images show strong variance (e.g. illumination, color, staining quality) due to differences in image acquisition, tissue processing, staining, etc. This can impede downstream image analysis such as staining intensity evaluation or classification. Methods to reduce th...

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Bibliographic Details
Main Authors: Marlen Runz, Daniel Rusche, Stefan Schmidt, Martin R. Weihrauch, Jürgen Hesser, Cleo-Aron Weis
Format: Article
Language:English
Published: BMC 2021-08-01
Series:Diagnostic Pathology
Subjects:
Histology stain normalization
HE-stain
Digital pathology
Generative adversarial networks
Unpaired image-to-image translation
Style transfer
Online Access:https://doi.org/10.1186/s13000-021-01126-y

Internet

https://doi.org/10.1186/s13000-021-01126-y

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