Studying cerebral vasculature using structure proximity and graph kernels.
Studying cerebral vasculature using structure proximity and graph kernels.
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使用结构邻近性和图核研究脑血管系统。
DOI：
10.1007/978-3-642-40763-5_66
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Aylward,Stephen
中科院分区：
文献类型：
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作者：
Kwitt,Roland;Pace,Danielle;Niethammer,Marc;Aylward,Stephen
提出了一种研究脑血管系统人群差异的方法。这是通过1)扩展将脑血管网络编码为空间图的概念和2)在基于核的判别分类器设置中量化图的相似性来实现的。我们认为，增加图形顶点与所选大脑结构的接近度信息增加了判别信息，从而导致更具表现力的编码。使用图核可以让我们以一种有原则的方式量化图的相似性。为了证明我们的方法，我们评估了性别差异表现为脑血管结构变化的假设，这一观察结果以前只在威利斯圆环中进行了测试和证实。我们的结果有力地支持了这一假设，即我们可以在40名健康患者的交叉验证设置中证明与随机性别分类的非平凡的、统计上显著的偏差。
An approach to study population differences in cerebral vasculature is proposed. This is done by 1) extending the concept of encoding cerebral blood vessel networks as spatial graphs and 2) quantifying graph similarity in a kernel-based discriminant classifier setup. We argue that augmenting graph vertices with information about their proximity to selected brain structures adds discriminative information and consequently leads to a more expressive encoding. Using graph-kernels then allows us to quantify graph similarity in a principled way. To demonstrate our approach, we assess the hypothesis that gender differences manifest as variations in the architecture of cerebral blood vessels, an observation that previously had only been tested and confirmed for the Circle of Willis. Our results strongly support this hypothesis, i.e, we can demonstrate non-trivial, statistically significant deviations from random gender classification in a cross-validation setup on 40 healthy patients.