Automatic cell detection and segmentation from H and E stained pathology slides using colorspace decorrelation stretching

Abstract

Purpose: Automatic cell segmentation plays an important role in reliable diagnosis and prognosis of patients. Most of the state-of-the-art cell detection and segmentation techniques focus on complicated methods to subtract foreground cells from the background. In this study, we introduce a preprocessing method which leads to a better detection and segmentation results compared to a well-known state-of-the-art work. Method: We transform the original red-green-blue (RGB) space into a new space defined by the top eigenvectors of the RGB space. Stretching is done by manipulating the contrast of each pixel value to equalize the color variances. New pixel values are then inverse transformed to the original RGB space. This altered RGB image is then used to segment cells. Result: The validation of our method with a well-known state-of-the-art technique revealed a statistically significant improvement on an identical validation set. We achieved a mean F1-score of 0.901. Conclusion: Preprocessing steps to decorrelate colorspaces may improve cell segmentation performances.