AUTOMATIC AIRWAY ANALYSIS FOR GENOME-WIDE ASSOCIATION STUDIES IN COPD.
AUTOMATIC AIRWAY ANALYSIS FOR GENOME-WIDE ASSOCIATION STUDIES IN COPD.
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DOI：
10.1109/isbi.2012.6235848
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Washko GG
中科院分区：
文献类型：
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作者：
Estépar RS;Ross JC;Kindlmann GL;Diaz A;Okajima Y;Kikinis R;Westin CF;Silverman EK;Washko GG
我们提出了一种适用于大规模遗传和流行病学研究（包括慢性阻塞性肺疾病（COPD）的全基因组关联研究（GWAS））的气道表型提取的图像管道。我们使用尺度空间粒子在一个大的高分辨率CT扫描队列中密集采样实质内气道位置。粒子方法是基于一个约束的能量最小化问题，结果在一组候选气道点位于物理空间和规模。这些点使用连接组件过滤进一步聚类，以增加其特异性。最后，我们使用粒子的位置来执行气道壁检测使用的边缘检测器的基础上的二阶导数的零交叉。给定气道壁位置，我们计算气道疾病的三种表型：壁增厚（Pi 10，WA %）和管腔重塑（P%）。我们验证了气道提取技术，并在2,500次扫描中展示了提取的表型与临床结果的相关性，这些结果将作为COPDGene研究GWAS分析的一部分进行部署。
We present an image pipeline for airway phenotype extraction suitable for large-scale genetic and epidemiological studies including genome-wide association studies (GWAS) in Chronic Obstructive Pulmonary Disease (COPD). We use scale-space particles to densely sample intraparenchymal airway locations in a large cohort of high-resolution CT scans. The particle methodology is based on a constrained energy minimization problem that results in a set of candidate airway points situated in both physical space and scale. Those points are further clustered using connected components filtering to increase their specificity. Finally, we use the particle locations to perform airway wall detection using an edge detector based on the zero-crossing of the second order derivative. Given the airway wall locations, we compute three phenotypes for airway disease: wall thickening (Pi10,WA%) and luminal remodeling (P%). We validate the airway extraction technique and present results in 2,500 scans for the association of the extracted phenotypes with clinical outcomes that will be deployed as part of the COPDGene study GWAS analysis.