Quantitative metric profiles capture three-dimensional temporospatial architecture to discriminate cellular functional states.
Quantitative metric profiles capture three-dimensional temporospatial architecture to discriminate cellular functional states.
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DOI：
10.1186/1471-2342-11-11
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发表时间：
2011-05-20
影响因子：
2.7
通讯作者：
Plopper GE
中科院分区：
文献类型：
--
作者：
McKeen-Polizzotti L;Henderson KM;Oztan B;Bilgin CC;Yener B;Plopper GE
组织结构的计算分析通过提取建立诊断概况的定量签名特征来揭示组织功能状态的亚视觉差异。不完整和/或不准确的配置文件有助于误诊。为了创建更完整的组织结构概况，我们调整了我们的细胞图方法，从组织病理学图像中提取定量特征，以捕获三维胶原蛋白水凝胶细胞培养物的时空特征。细胞图是利用图论来描述组织中细胞之间的空间结构的一种图，其中细胞的核构成节点，细胞之间的近似邻接关系用边表示。我们从多种组织来源中选择了11种不同的细胞类型，代表非致瘤性，癌前和恶性状态。我们从细胞水凝胶图像中构建细胞图，并计算了大量描述随时间推移由图捕获的结构特征的特征。使用三模式张量分析，我们确定了五个最重要的特征（度量），这些特征捕获了整个时间过程中每种细胞类型的3D结构变化的紧凑性，聚类性和空间均匀性。重要的是，这些指标中的四个也是我们以前研究的组织病理学数据的判别特征。总之，这些描述性度量提供了其他图像分析方法所不能提供的图像信息的严格定量表示。检查这五个指标的变化使我们能够轻松区分所有11种细胞类型，而图像视觉检查的差异并不明显。这些结果表明，应用细胞图技术的3D图像数据产生的判别指标，有可能提高基于图像的组织轮廓的准确性，从而提高疾病的检测和诊断。
Computational analysis of tissue structure reveals sub-visual differences in tissue functional states by extracting quantitative signature features that establish a diagnostic profile. Incomplete and/or inaccurate profiles contribute to misdiagnosis. In order to create more complete tissue structure profiles, we adapted our cell-graph method for extracting quantitative features from histopathology images to now capture temporospatial traits of three-dimensional collagen hydrogel cell cultures. Cell-graphs were proposed to characterize the spatial organization between the cells in tissues by exploiting graph theory wherein the nuclei of the cells constitute the nodes and the approximate adjacency of cells are represented with edges. We chose 11 different cell types representing non-tumorigenic, pre-cancerous, and malignant states from multiple tissue origins. We built cell-graphs from the cellular hydrogel images and computed a large set of features describing the structural characteristics captured by the graphs over time. Using three-mode tensor analysis, we identified the five most significant features (metrics) that capture the compactness, clustering, and spatial uniformity of the 3D architectural changes for each cell type throughout the time course. Importantly, four of these metrics are also the discriminative features for our histopathology data from our previous studies. Together, these descriptive metrics provide rigorous quantitative representations of image information that other image analysis methods do not. Examining the changes in these five metrics allowed us to easily discriminate between all 11 cell types, whereas differences from visual examination of the images are not as apparent. These results demonstrate that application of the cell-graph technique to 3D image data yields discriminative metrics that have the potential to improve the accuracy of image-based tissue profiles, and thus improve the detection and diagnosis of disease.