计算机应用 ›› 2015, Vol. 35 ›› Issue (12): 3570-3575.DOI: 10.11772/j.issn.1001-9081.2015.12.3570

• 虚拟现实与数字媒体 • 上一篇    下一篇

基于多特征描述的乳腺癌肿瘤病理自动分级

龚磊1, 徐军1, 王冠皓1, 吴建中2, 唐金海2   

  1. 1. 南京信息工程大学江苏省大数据分析技术重点实验室, 南京 210044;
    2. 江苏省肿瘤医院, 南京 210000
  • 收稿日期:2015-05-18 修回日期:2015-08-01 出版日期:2015-12-10 发布日期:2015-12-10
  • 通讯作者: 徐军(1972-),男,江西乐平人,教授,博士生导师,CCF会员,主要研究方向:计算机视觉、机器学习、癌症的计算机辅助检测、诊断与预后
  • 作者简介:龚磊(1991-),男,江苏扬州人,硕士研究生,主要研究方向:病理图像分析、癌症的计算机辅助诊断与预后;王冠皓(1989-),男,江苏徐州人,硕士研究生,主要研究方向:深度学习、压缩感知、稀疏表示;吴建中(1965-),男,江苏南通人,研究员,主要研究方向:肿瘤流行病学、肿瘤遗传学;唐金海(1961-),男,江苏连云港人,教授,博士生导师,博士,主要研究方向:乳腺肿瘤的临床诊治与研究。
  • 基金资助:
    国家自然科学基金资助项目(61273259);江苏省"六大人才高峰"高层次人才项目(2013-XXRJ-019);江苏省自然科学基金资助项目(BK20141482)。

Multi-feature based descriptions for automated grading on breast histopathology

GONG Lei1, XU Jun1, WANG Guanhao1, WU Jianzhong2, TANG Jinhai2   

  1. 1. Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing Jiangsu 210044, China;
    2. Jiangsu Cancer Hospital, Nanjing Jiangsu 210000, China
  • Received:2015-05-18 Revised:2015-08-01 Online:2015-12-10 Published:2015-12-10

摘要: 为了辅助病理医生快速高效诊断乳腺癌并提供乳腺癌预后信息,提出一种计算机辅助乳腺癌肿瘤病理自动分级方法。该方法使用深度卷积神经网络和滑动窗口自动检测病理图像中的细胞;随后综合运用基于稀疏非负矩阵分解的颜色分离、前景标记的分水岭算法以及椭圆拟合得到每个细胞的轮廓。基于检测到的细胞和拟合出的细胞轮廓,提取出肿瘤的组织结构特征和上皮细胞的纹理形状特征等共203维的特征,运用这些特征训练支持向量机分类器(SVM),实现对病理组织图像自动分级。17位患者的49张H&E染色的乳腺癌病理组织图像自动分级的100次十折交叉检验评估结果表明:基于病理图像的细胞形状特征与组织的空间结构特征对病理图像的高、中、低分化等级分类整体准确率为90.20%;同时对高、中、低各分化等级的区分准确率分别为92.87%、82.88%、93.61%。相比使用单一结构特征或者纹理特征的方法,所提方法具有更高的准确率,能准确地对病理组织图像中肿瘤的高级和低级分化程度自动分级,且各分级之间的准确率差异较小。

关键词: 乳腺癌, 组织病理图像, 自动病理分级, 计算机辅助预后分析

Abstract: In order to assist in the fast and efficient diagnosis of breast cancer and provide the prognosis information for pathologists, a computer-aided diagnosis approach for automatically grading breast pathological images was proposed. In the proposed algorithm,cells of pathological images were first automatically detected by deep convolutional neural network and sliding window. Then, the algorithms of color separation based on sparse non-negative matrix factorization, marker controlled watershed, and ellipse fitting were integrated to get the boundary of each cell. A total of 203-dimensional image-derived features, including architectural features of tumor, texture and shape features of epithelial cells were extracted from the pathological images based on the detected cells and the fitted boundary. A Support Vector Machine (SVM) classifier was trained by using the extracted features to realize the automated grading of pathological images. In order to verify the proposed algorithm, a total of 49 Hematoxylin & Eosin (H&E)-stained breast pathological images obtained from 17 patients were considered. The experimental results show that,for 100 ten-fold cross-validation trials, the features with the cell shape and the spatial structure of organization of pathological image set successfully distinguish test samples of low, intermediate and high grades with classification accuracy of 90.20%. Moreover, the proposed algorithm is able to distinguish high grade, intermediate grade, and low grade patients with accuracy of 92.87%, 82.88% and 93.61%, respectively. Compared with the methods only using texture feature or architectural feature, the proposed algorithm has a higher accuracy. The proposed algorithm can accurately distinguish the grade of tumor for pathological images and the difference of accuracy between grades is small.

Key words: breast cancer, pathological image, automated pathological grading, computer-aided prognosis

中图分类号: