Automated segmentation and quantification of airway mucus with endobronchial optical coherence tomography.
Automated segmentation and quantification of airway mucus with endobronchial optical coherence tomography.
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DOI：
10.1364/boe.8.004729
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发表时间：
2017-10
影响因子：
3.4
通讯作者：
D. C. Adams;H. Pahlevaninezhad;M. Szabari;Josalyn L. Cho;D. Hamilos;M. Kesimer;R. Boucher;A. Luster;B. Medoff;M. Suter
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文献类型：
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作者：
D. C. Adams;H. Pahlevaninezhad;M. Szabari;Josalyn L. Cho;D. Hamilos;M. Kesimer;R. Boucher;A. Luster;B. Medoff;M. Suter
我们提出了一套新的算法，用于在支气管内光学相干断层扫描（OCT）数据集中自动分割气道腔和粘液，以及一种新的方法来量化粘液的内容。黏液和管腔使用鲁棒的多阶段算法进行分割，只需要最小的鞘几何输入。该算法在大范围的气道和噪声条件下具有较高的精度。采用平均后向散射强度和灰度共生矩阵（GLCM）统计对黏液进行分类。我们在哮喘和非哮喘志愿者体内评估了我们的技术。
We propose a novel suite of algorithms for automatically segmenting the airway lumen and mucus in endobronchial optical coherence tomography (OCT) data sets, as well as a novel approach for quantifying the contents of the mucus. Mucus and lumen were segmented using a robust, multi-stage algorithm that requires only minimal input regarding sheath geometry. The algorithm performance was highly accurate in a wide range of airway and noise conditions. Mucus was classified using mean backscattering intensity and grey level co-occurrence matrix (GLCM) statistics. We evaluated our techniques in vivo in asthmatic and non-asthmatic volunteers.