  Stain color variation in histological images, caused by a variety of factors,
is a challenge not only for the visual diagnosis of pathologists but also for
cell segmentation algorithms. To eliminate the color variation, many stain
normalization approaches have been proposed. However, most were designed for
hematoxylin and eosin staining images and performed poorly on
immunohistochemical staining images. Current cell segmentation methods
systematically apply stain normalization as a preprocessing step, but the
impact brought by color variation has not been quantitatively investigated yet.
In this paper, we produced five groups of NeuN staining images with different
colors. We applied a deep learning image-recoloring method to perform color
transfer between histological image groups. Finally, we altered the color of a
segmentation set and quantified the impact of color variation on cell
segmentation. The results demonstrated the necessity of color normalization
prior to subsequent analysis.

Image and Video Processing;Computer Vision and Pattern Recognition