Jørgensen Alex Skovsbo, Rasmussen Anders Munk, Andersen Niels Kristian Mäkinen, Andersen Simon Kragh, Emborg Jonas, Røge Rasmus, Østergaard Lasse Riis
Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
Diagnostics & Genomics Group, Dako Denmark A/S, An Agilent Technologies Company, Glostrup, Denmark.
Cytometry A. 2017 Aug;91(8):785-793. doi: 10.1002/cyto.a.23175. Epub 2017 Jul 20.
Currently, diagnosis of colon cancer is based on manual examination of histopathological images by a pathologist. This can be time consuming and interpretation of the images is subject to inter- and intra-observer variability. This may be improved by introducing a computer-aided diagnosis (CAD) system for automatic detection of cancer tissue within whole slide hematoxylin and eosin (H&E) stains. Cancer disrupts the normal control mechanisms of cell proliferation and differentiation, affecting the structure and appearance of the cells. Therefore, extracting features from segmented cell nuclei structures may provide useful information to detect cancer tissue. A framework for automatic classification of regions of interest (ROI) containing either benign or cancerous colon tissue extracted from whole slide H&E stained images using cell nuclei features was proposed. A total of 1,596 ROI's were extracted from 87 whole slide H&E stains (44 benign and 43 cancer). A cell nuclei segmentation algorithm consisting of color deconvolution, k-means clustering, local adaptive thresholding, and cell separation was performed within the ROI's to extract cell nuclei features. From the segmented cell nuclei structures a total of 750 texture and intensity-based features were extracted for classification of the ROI's. The nine most discriminative cell nuclei features were used in a random forest classifier to determine if the ROI's contained benign or cancer tissue. The ROI classification obtained an area under the curve (AUC) of 0.96, sensitivity of 0.88, specificity of 0.92, and accuracy of 0.91 using an optimized threshold. The developed framework showed promising results in using cell nuclei features to classify ROIs into containing benign or cancer tissue in H&E stained tissue samples. © 2017 International Society for Advancement of Cytometry.
目前，结肠癌的诊断是由病理学家对组织病理学图像进行人工检查。这可能很耗时，而且图像的解读会受到观察者间和观察者内差异的影响。通过引入计算机辅助诊断（CAD）系统来自动检测全切片苏木精和伊红（H&E）染色中的癌组织，这种情况可能会得到改善。癌症会破坏细胞增殖和分化的正常控制机制，影响细胞的结构和外观。因此，从分割后的细胞核结构中提取特征可能为检测癌组织提供有用信息。提出了一个使用细胞核特征对从全切片H&E染色图像中提取的包含良性或癌性结肠组织的感兴趣区域（ROI）进行自动分类的框架。从87张全切片H&E染色（44例良性和43例癌症）中总共提取了1596个ROI。在ROI内执行了一种由颜色反卷积、k均值聚类、局部自适应阈值处理和细胞分离组成的细胞核分割算法，以提取细胞核特征。从分割后的细胞核结构中总共提取了750个基于纹理和强度的特征，用于ROI的分类。九个最具区分性的细胞核特征被用于随机森林分类器中，以确定ROI是否包含良性或癌组织。使用优化阈值，ROI分类的曲线下面积（AUC）为0.96，灵敏度为0.88，特异性为0.92，准确率为0.91。所开发的框架在使用细胞核特征将H&E染色组织样本中的ROI分类为包含良性或癌组织方面显示出了有前景的结果。© 2017国际细胞计量学促进协会。
