Improving Accuracy of Nuclei Segmentation by Reducing Histological Image Variability

Histological analyses of tissue biopsies is an essential component in the diagnosis of several diseases including cancer. In the past, evaluation of tissue samples was done manually, but to improve efficiency and ensure consistent quality, there has been a push to evaluate these algorithmically. One important task in histological analysis is the segmentation and evaluation of nuclei. Nuclear morphology is important to understand the grade and progression of disease. However, implementing automated methods at scale across histological datasets is challenging due to differences in stain, slide preparation and slide storage. This paper evaluates the impact of four stain normalization methods on the performance of nuclei segmentation algorithms. The goal is to highlight the critical role of stain normalization in improving the usability of learning-based models (such as convolutional neural networks (CNNs)) for this task. Using stain normalization, the baseline segmentation accuracy across distinct training and test datasets was improved by more than 50% of its base value as measured by the AUC and Recall. We believe this is the first study to perform a comparative analysis of four stain normalization approaches (histogram equalization, Reinhart, Macenko, spline mapping) on segmentation accuracy of CNNs.