Stain normalization methods for histopathology image analysis: A comprehensive review and experimental comparison

The advent of whole slide imaging has brought advanced computer-aided diagnosis via medical imaging and artificial intelligence technologies in digital pathology. The examination of tissue samples through whole slide imaging is commonly used to diagnose cancerous diseases, but the analysis of histopathology images through a decision support system is not always accurate due to variations in color caused by different scanning equipment, staining methods, and tissue reactivity. These variabilities decrease the accuracy of computer-aided diagnosis and affect the diagnosis of pathologists. In this context, an effective stain normalization method has proved as a powerful tool to standardize different color appearances and minimize color variations in histopathology images. This study reviews different stain normalization methods highlighting the main methodologies, contributions, advantages, and limitations of correlated works. The state-of-the-art methods are grouped into four distinct categories. Next, we select ten representative methods from the groups and conduct an experimental comparison to investigate the strengths and weaknesses of different methods and rank them according to selected performance accuracy measures. The quality performances of selected methods are compared in terms of quaternion structure similarity index metric, structural similarity index metric, and pearson correlation coefficient conducting experiments on three histopathology datasets. Our findings conclude that the structure-preserving unified transformation-based methods consistently outperform the state-of-the-art methods by improving robustness against variability and reproducibility. The comparative analysis we conducted in this paper will serve as the basis for future research, which will help to refine existing techniques and develop new approaches to address the complexities of stain normalization in complex histopathology images.