基于局部可变阈值的无监督定量Ki-67细胞增殖指数模型的初步建立

Preliminary establishment of an unsupervised quantification model of Ki-67 proliferation index based on local variable threshold method

目的:开发一种高效率基于Ki-67细胞增殖指数的自动定量模型。方法:选择口腔鳞癌组织切片进行Ki-67免疫组织化学标记。通过全玻片成像技术获得数字化彩色病理图像,采用基于局部统计的可变阈值方法将图像中的无关浅色背景区域去掉,保留胞核部分。在L*a*b*颜色空间中,采用阈值方法对蓝色(苏木精染色)和棕色(Ki67染色)进行分离,计算增殖指数。采用SPSS 24.0软件包中的配对t检验和Spearman相关系数与人工计数和机器学习中常用的颜色反卷积方法进行比较分析。结果:建立了一种新的胞核检测和分类模型,可以处理不同大小的病理图像,并有效检测出免疫组织化学阳性细胞和阴性细胞,结果与人工计数结果无显著差异(P>0.05),但计算速度较快,在处理较大尺寸图像时计算效率优势更加明显,且检测结果优于常用的颜色反卷积方法(P<0.05)。结论:本研究开发的胞核自动定量分析模型可高效分析口腔鳞癌细胞核Ki-67染色情况,计算相应增殖指数,辅助病理诊断。

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 SPSS24.0 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 counting(P>0.05), 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 method(P<0.05). 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.