ECM-Aware Cell-Graph Mining for Bone Tissue Modeling and Classification.
ECM-Aware Cell-Graph Mining for Bone Tissue Modeling and Classification.
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DOI：
10.1007/s10618-009-0153-2
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发表时间：
2009-10-21
影响因子：
4.8
通讯作者：
Yener, Buelent
中科院分区：
文献类型：
--
作者：
Bilgin, Cemal Cagatay;Bullough, Peter;Plopper, George E.;Yener, Buelent
活检的病理检查是诊断骨癌最可靠和最广泛使用的技术。然而，它同时存在观察者之间和观察者内部的主观性。自动组织建模和分类技术可以减少这种主观性，提高骨癌诊断的准确性。本文提出了一种图论方法，称为细胞外基质（ECM）感知的细胞图挖掘，结合细胞的分布在苏木精和伊红（H&E）染色的骨组织样本的组织病理学图像的ECM的形成。这种方法可以识别由于其功能状态而共存于同一组织中的不同类型的细胞。因此，它可以更精确地模拟结构-功能关系，并准确地对骨组织样本进行分类，用于癌症诊断。利用Hessian矩阵的特征值对组织图像进行分割，计算细胞核的空间坐标作为相应细胞图的节点。在分割时，基于其周围ECM的组成将颜色代码分配给每个节点。假设（并建立）一对节点之间的边缘，如果相应的细胞膜是物理接触，如果它们共享相同的颜色。因此，多个着色细胞图共存于组织中，每个着色细胞图对不同的细胞类型组织进行建模。ECM感知细胞图的拓扑和光谱特征都被计算以量化组织样本的结构特性，并使用支持向量机将其不同的功能状态分类为健康、骨折或癌症。与相关工作的分类准确率比较表明，ECM感知细胞图方法的准确率为90.0%，而Delaunay三角剖分和简单细胞图方法的准确率分别为75.0%和81.1%。
Pathological examination of a biopsy is the most reliable and widely used technique to diagnose bone cancer. However, it suffers from both inter- and intra- observer subjectivity. Techniques for automated tissue modeling and classification can reduce this subjectivity and increases the accuracy of bone cancer diagnosis. This paper presents a graph theoretical method, called extracellular matrix (ECM)-aware cell-graph mining, that combines the ECM formation with the distribution of cells in hematoxylin and eosin (H&E) stained histopathological images of bone tissues samples. This method can identify different types of cells that coexist in the same tissue as a result of its functional state. Thus, it models the structure-function relationships more precisely and classifies bone tissue samples accurately for cancer diagnosis. The tissue images are segmented, using the eigenvalues of the Hessian matrix, to compute spatial coordinates of cell nuclei as the nodes of corresponding cell-graph. Upon segmentation a color code is assigned to each node based on the composition of its surrounding ECM. An edge is hypothesized (and established) between a pair of nodes if the corresponding cell membranes are in physical contact and if they share the same color. Hence, multiple colored-cell-graphs coexist in a tissue each modeling a different cell-type organization. Both topological and spectral features of ECM-aware cell-graphs are computed to quantify the structural properties of tissue samples and classify their different functional states as healthy, fractured, or cancerous using support vector machines. Classification accuracy comparison to related work shows that ECM-aware cell-graph approach yields 90.0% whereas Delaunay triangulation and simple cell-graph approach achieves 75.0% and 81.1% accuracy, respectively.
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影响因子：
56.9
作者：
Golub, TR;Slonim, DK;Lander, ES
通讯作者：
Lander, ES
影响因子：
1.7
作者：
Ben-Dor, A;Bruhn, L;Yakhini, Z
通讯作者：
Yakhini, Z
影响因子：
19.5
作者：
Hodneland, Erlend;Tai, Xue-Cheng;Gerdes, Hans-Hermann
通讯作者：
Gerdes, Hans-Hermann
影响因子：
10.6
作者：
Ganster, H;Pinz, A;Kittler, H
通讯作者：
Kittler, H
DOI：
10.1109/4233.992163
发表时间：
2002-03-01
影响因子：
--
作者：
Esgiar, AN;Naguib, RNG;Murray, A
通讯作者：
Murray, A