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An Automated and Accurate Methodology to Assess Ki-67 Labeling Index of Immunohistochemical Staining Images of Breast Cancer Tissues

Automatic scoring of Ki-67 with digital image analysis would improve the accuracy of the diagnostic. However, automatic Ki-67 assessment is very challenging due to complex variations of cell characteristics. In this paper, we propose an integrated framework for accurate Ki-67 scoring. The main contributions of our method are: a robust cell detection algorithm to detect all tumor and non-tumor cells; a clustering model for computerized classification of tumor and non-tumor cells and subsequent proliferation rate scoring by quantifying Ki-67, based on classified cells which appear in breast cancer immunohistochemical images. Unlike existing Ki-67 scoring techniques, our methodology works on whole slide images (WSI) using patches that are extracted from detected tissues. As the size of each sample is so large they can not be handled as a single image. Therefore, each slide is divided into small parts and on edge tiles merging is considered to preserve the continuity of nuclei. The proposed method has been extensively evaluated on tissue microarray (TMA) whole slides, and the cell detection performance is comparable to manual annotations and is very accurate compared with the estimation of an experienced pathologist.