Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
IEEE Trans Med Imaging. 2013 Apr;32(4):670-82. doi: 10.1109/TMI.2012.2231420. Epub 2012 Dec 4.
Automated analysis of whole mount tissue sections can provide insights into tumor subtypes and the underlying molecular basis of neoplasm. However, since tumor sections are collected from different laboratories, inherent technical and biological variations impede analysis for very large datasets such as The Cancer Genome Atlas (TCGA). Our objective is to characterize tumor histopathology, through the delineation of the nuclear regions, from hematoxylin and eosin (H&E) stained tissue sections. Such a representation can then be mined for intrinsic subtypes across a large dataset for prediction and molecular association. Furthermore, nuclear segmentation is formulated within a multi-reference graph framework with geodesic constraints, which enables computation of multidimensional representations, on a cell-by-cell basis, for functional enrichment and bioinformatics analysis. Here, we present a novel method, multi-reference graph cut (MRGC), for nuclear segmentation that overcomes technical variations associated with sample preparation by incorporating prior knowledge from manually annotated reference images and local image features. The proposed approach has been validated on manually annotated samples and then applied to a dataset of 377 Glioblastoma Multiforme (GBM) whole slide images from 146 patients. For the GBM cohort, multidimensional representation of the nuclear features and their organization have identified 1) statistically significant subtypes based on several morphometric indexes, 2) whether each subtype can be predictive or not, and 3) that the molecular correlates of predictive subtypes are consistent with the literature. Data and intermediaries for a number of tumor types (GBM, low grade glial, and kidney renal clear carcinoma) are available at: http://tcga.lbl.gov for correlation with TCGA molecular data. The website also provides an interface for panning and zooming of whole mount tissue sections with/without overlaid segmentation results for quality control.
全自动分析全组织切片能够深入了解肿瘤亚型和肿瘤发生的潜在分子基础。然而，由于肿瘤切片来自不同的实验室，固有的技术和生物学差异会阻碍对大型数据集（如癌症基因组图谱（TCGA））的分析。我们的目标是通过对苏木精和伊红（H&E）染色组织切片的核区域进行描绘，来描述肿瘤组织病理学。然后，可以对大量数据集进行内在亚型的挖掘，以进行预测和分子关联。此外，核分割是在具有测地线约束的多参考图框架内进行的，这使得能够在细胞基础上计算多维表示，以进行功能富集和生物信息学分析。在这里，我们提出了一种新的方法，多参考图切割（MRGC），用于核分割，该方法通过结合手动注释参考图像和局部图像特征的先验知识，克服了与样本制备相关的技术差异。该方法已经在手动注释样本上进行了验证，然后应用于来自 146 名患者的 377 例胶质母细胞瘤（GBM）全幻灯片图像数据集。对于 GBM 队列，核特征及其组织的多维表示确定了 1）基于几个形态计量指标的具有统计学意义的亚型，2）每个亚型是否可以进行预测，以及 3）预测亚型的分子相关性与文献一致。多种肿瘤类型（GBM、低级别神经胶质瘤和肾透明细胞癌）的数据和中间产物可在 http://tcga.lbl.gov 获得，以便与 TCGA 分子数据进行关联。该网站还提供了一个接口，用于带/不带叠加分割结果的全组织切片的平移和缩放，以便进行质量控制。
