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

doi:10.19439/j.sjos.2022.03.005

上海口腔医学 ›› 2022, Vol. 31 ›› Issue (3): 248-254.doi: 10.19439/j.sjos.2022.03.005

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

秦志明^1,2, 揭伟萍^1,2, 池彦廷^1,2, 李宏锋^3,*, 李斌斌^1,2,*

1.北京大学口腔医学院·口腔医院病理科,国家口腔疾病临床医学研究中心, 口腔数字化医疗技术和材料国家工程实验室,口腔数字医学北京市重点实验室,北京 100081;
2.中国医学科学院口腔颌面部肿瘤精准病理诊断创新单元,北京 100081;
3.北京大学健康医疗大数据研究中心,北京 100191

收稿日期:2021-07-02 修回日期:2021-11-02 出版日期:2022-06-25 发布日期:2022-07-07
通讯作者: 李斌斌,E-mail：kqlibinbin@bjmu.edu.cn;李宏锋,E-mail：lihongfeng@pku.edu.cn。^*共同通信作者
作者简介:秦志明（1996-）,男,在读硕士研究生,E-mail：qinzhiming96@bjmu.edu.cn
基金资助:
中国医学科学院医学与健康科技创新工程项目（2019-I2M-5-038）

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

QIN Zhi-ming^1,2, JIE Wei-ping^1,2, CHI Yan-ting^1,2, LI Hong-feng³, LI Bin-bin^1,2

1. Department of Oral Pathology, Peking University School and Hospital of Stomatology; National Clinical Research Center for Oral Diseases; National Engineering Laboratory for Digital and Material Technology of Stomatology; Beijing Key Laboratory of Digital Stomatology. Beijing 100081;
2. Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions, Chinese Academy of Medical Sciences. Beijing 100081;
3. Center for Data Science in Health and Medicine, Peking University. Beijing 100191, China

Received:2021-07-02 Revised:2021-11-02 Online:2022-06-25 Published:2022-07-07

摘要/Abstract

摘要： 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。

关键词: 口腔鳞癌, Ki-67, 增殖指数, 机器学习

Abstract: 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 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.

Key words: Oral squamous cell carcinoma, Ki-67, Proliferation index, Machine learning

中图分类号:

R739.8

秦志明, 揭伟萍, 池彦廷, 李宏锋, 李斌斌. 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[J]. 上海口腔医学, 2022, 31(3): 248-254.

QIN Zhi-ming, JIE Wei-ping, CHI Yan-ting, LI Hong-feng, LI Bin-bin. Preliminary establishment of an unsupervised quantification model of Ki-67 proliferation index based on local variable threshold method[J]. Shanghai Journal of Stomatology, 2022, 31(3): 248-254.

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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

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