Background:Haematopathological Ki-67 is used principally to measure the proliferation rate in the assessment and grading of malignancies. Ki-67 is based on a powerful staining method for distinguishing benign from malignant proliferation. The index uses a nuclear protein expression and it has been widely used to evaluate the proliferative activity of lymphoma. The clinical value of Ki-67 includes defining prognosis (among lymphomas), predicting drug response, and setting eligibility criteria for clinical trials. The Ki-67 score or index should be expressed as the percentage of positively stained cells among the total number of invasive cells in the area scored. With the Ki-67 marker, the proliferation fraction of low-grade follicular lymphomas (FLs) is usually less than 20% (as shown here) and that of high-grade FLs is greater than 30% . Manual Ki-67 proliferation assessment is a very time-consuming and operator-dependent task at the same time. Therefore, several studies have examined the use of image analysis software to measure faster the nuclear staining index of Ki-67 in lymphomas. A few studies have focused on the measurement of proliferation index in FLs and found that automated Ki-67 counts were similar to manual counts [2-3]. A major source of difference between automatic and manual Ki-67 scores is the scoring method that depends on the strategy of counting or the estimation and choice of the area to count.
Methods:In this research, an automatic unsupervised learning-based system was proposed for accurate and fast Ki-67 scoring in lymphoma. The proposed methods were designed to use image processing tools and detect robustly the positive and negative cells for Ki-67 antibody. The goal of the proposed method was to assess the proliferation index (percentage of Ki-67 positive lymphoma cells) to provide better treatment options for lymphoma patients. The proposed system consisted of the following sections: pre-processing, feature extraction, segmentation, and post-processing (Figure 1). To highlight specific histological structures of Ki-67 stained images such as positives cells (brown color ones), we performed pre-processing such as color transform from RGB space to brown-ratio space. For smoothing and filling the region of each cell on the image, the morphological filling was used. After the pre-processing section, color features, such as the mean of brown-ratio color space and blue channel of RGB image in a 3 × 3 block, were extracted from the image. In the next section of the proposed system, using the extracted color features, the image was segmented into three clusters by k-means clustering. After image segmentation, each of the positive and negative cells was post-processed. In the post-processing section, to split the merged cell, the morphological opening was used and finally, false segmented regions with small areas were removed. To evaluate experiments, we used five Ki-67 stained whole slide images of lymphoma from the Pathology Department of the Medical University of Vienna (AKH).
Results:Figure 2 shows the results of the proposed system for a sample region of the Ki-67 stained image. The manually labeled positive and negative cells were considered as ground-truth. The ground-truth was compared with the automatically segmented cells obtained from the proposed system. The computational complexity of the proposed system was very low so that the average time needed for the assessment of a high-power field image with a resolution of 1239 × 1239 was 5.7 seconds using a workstation with a 2.50 GHz Intel® i7 - 2450 CPU and a 16 GB memory. The efficiency of the proposed system was evaluated for estimating the Ki-67 index from Ki-67 stained whole slide images. A dataset was used containing five whole slide images. Figure 3 shows one of the whole slide images. To compare the results, each slide was analyzed by an expert pathologist and the Ki-67 index was estimated manually. Table 1 shows the performance of the proposed system for the Ki-67 index estimation. The results confirmed the efficacy of the proposed system for Ki-67 index estimation from whole slide images.
Conclusion:The detailed experimental analysis reflected the promising results of Ki-67 scoring based on the proposed system.
Lymphoma Cancer Ki-67 Proliferation Index Image Clustering Immunohistochemistry
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