一种基于高分辨率平铺的方法用于对全切片组织病理学图像中的生物区域进行分类。
A High-Resolution Tile-Based Approach for Classifying Biological Regions in Whole-Slide Histopathological Images.
作者信息
Hoffman R A, Kothari S, Phan J H, Wang M D
机构信息
Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
出版信息
IFMBE Proc. 2014;42:280-283. doi: 10.1007/978-3-319-03005-0_71.
Computational analysis of histopathological whole slide images (WSIs) has emerged as a potential means for improving cancer diagnosis and prognosis. However, an open issue relating to the automated processing of WSIs is the identification of biological regions such as tumor, stroma, and necrotic tissue on the slide. We develop a method for classifying WSI portions (512x512-pixel tiles) into biological regions by (1) extracting a set of 461 image features from each WSI tile, (2) optimizing tile-level prediction models using nested cross-validation on a small (600 tile) manually annotated tile-level training set, and (3) validating the models against a much larger (1.7x10 tile) data set for which ground truth was available on the whole-slide level. We calculated the predicted prevalence of each tissue region and compared this prevalence to the ground truth prevalence for each image in an independent validation set. Results show significant correlation between the predicted (using automated system) and reported biological region prevalences with p < 0.001 for eight of nine cases considered.
组织病理学全切片图像（WSIs）的计算分析已成为改善癌症诊断和预后的一种潜在手段。然而，与WSIs自动处理相关的一个未解决问题是识别玻片上的生物区域，如肿瘤、基质和坏死组织。我们开发了一种将WSI部分（512x512像素切片）分类到生物区域的方法，具体步骤如下：（1）从每个WSI切片中提取一组461个图像特征；（2）在一个小的（600个切片）手动注释的切片级训练集上使用嵌套交叉验证优化切片级预测模型；（3）在一个大得多的（1.7×10个切片）数据集上验证模型，该数据集在全切片级别上有真实数据可用。我们计算了每个组织区域的预测患病率，并将该患病率与独立验证集中每个图像的真实患病率进行比较。结果显示，在考虑的九个病例中的八个病例中，预测（使用自动化系统）的和报告的生物区域患病率之间存在显著相关性，p < 0.001。
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