Evaluating the Impact of Color Normalization on Kidney Image Segmentation


Sai Javvadi, University of Louisville, Louisville Kentucky, USA


The role of deep learning in the recognition of morphological structures in histopathological data has progressed significantly. But, less intensive preprocessing stages and their contribution to deep learning pipelines is often overlooked. Color normalization (CN) algorithms are among the most prominent methods in this stage, and they work by standardizing the staining pattern of a dataset. However, the impact of various color normalization algorithms on the detection of glomeruli functional tissue units (FTUs) in kidney tissue data has not been explored before. An advanced deep learning architecture was built with the U-NET segmentation model. The U-NET model is an architecture that specializes in the segmentation of biomedical data. A dataset of 15 kidney whole slide images (WSIs), each annotated with locations of glomeruli FTUs were processed and subsequently normalized according to three 3 different conventional color normalization techniques (Reinhard, Vahadane, Macenko), and fed into a U-NET model. The dice score coefficient (DSC) was used to compare the results of each run. It was determined that color normalization algorithms significantly impact the segmentation results of deep learning algorithms, with the Reinhard algorithm being the best technique. The implications of this work are immense, as it could contribute to the proliferation of color normalization techniques in preprocessing deep learning workflows, which could improve general segmentation accuracies.


Deep Learning, Color Normalization, Histopathology, Kidney, Glomeruli