RESUMEN
PURPOSE: To develop an effective machine learning method for estimation of Ki-67 cell proliferation index. METHODS: Oral squamous cell carcinoma(OSCC) slices were selected for Ki-67 immunohistochemical staining. The digital pathology images were obtained through whole-slide imaging technology. Variable threshold method based on local statistics was applied to preprocess the images, aiming at reducing the noise in the images. Adaptive threshold method was used to remove the irrelevant light-colored background area in the image, retaining the nucleus part. A threshold method in space was applied to differentiate brown from blue content. Finally, the proliferation index was estimated and compared with manual and the color deconvolution method by paired sample t test and spearman correlation coefficients with SPSS 24.0 software package. RESULTS: A new nucleus detection and classification method was established, which can process pathologic images of different sizes, and effectively detect immunohistochemical brown positive cells and blue negative cells. There was no significant difference between this algorithm and manual countingï¼Pï¼0.05ï¼, but the speed was faster. The calculation efficiency advantage was more obvious when processing a large image, and the detection result of Ki-67 proliferation index was better than the commonly used color deconvolution methodï¼Pï¼0.05ï¼. CONCLUSIONS: The automatic nucleus quantitative analysis method developed in this study can analyze Ki-67 staining of the nucleus in OSCC cells efficiently and calculate the proliferation index, which can be used for auxiliary diagnosis in pathology.
