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Effect of Color Normalization on Nuclei Segmentation Problem in H&E Stained Histopathology Images

Cancer is one of the most common causes of death today. Early diagnosis is of great importance for treatment. Early diagnosis is made by detecting and examining cancerous nuclei. For this reason, studies that automatically perform nuclei segmentation on histopathology images are given wide coverage in the literature to assist pathologists. There may be differences in histopathology images due to staining or external factors. In order to eliminate these differences, it becomes necessary to perform color normalization on the image before starting the segmentation process, both to standardize the images and to improve the results obtained. In this study, the effects of three different color normalization processes on the image were compared. The comparison process was carried out according to the nuclei segmentation results obtained by applying two different deep learning models, U-Net and Residual U-Net. As a result of the study, it has been observed that the training with the images with the color normalization process gives more successful results than the training with the original image.