从光学相干断层扫描图像中自动定量分析肺部结构。
Automated quantification of lung structures from optical coherence tomography images.
作者信息
Pagnozzi Alex M, Kirk Rodney W, Kennedy Brendan F, Sampson David D, McLaughlin Robert A
机构信息
Optical + Biomedical Engineering Laboratory, School of Electrical, Electronic and Computer Engineering, The University of Western Australia, 35 Stirling Highway, Crawley, Western Australia 6009, Australia.
出版信息
Biomed Opt Express. 2013 Oct 9;4(11):2383-95. doi: 10.1364/BOE.4.002383. eCollection 2013.
Characterization of the size of lung structures can aid in the assessment of a range of respiratory diseases. In this paper, we present a fully automated segmentation and quantification algorithm for the delineation of large numbers of lung structures in optical coherence tomography images, and the characterization of their size using the stereological measure of median chord length. We demonstrate this algorithm on scans acquired with OCT needle probes in fresh, ex vivo tissues from two healthy animal models: pig and rat. Automatically computed estimates of lung structure size were validated against manual measures. In addition, we present 3D visualizations of the lung structures using the segmentation calculated for each data set. This method has the potential to provide an in vivo indicator of structural remodeling caused by a range of respiratory diseases, including chronic obstructive pulmonary disease and pulmonary fibrosis.
肺结构大小的表征有助于评估一系列呼吸系统疾病。在本文中，我们提出了一种全自动分割和量化算法，用于在光学相干断层扫描图像中描绘大量肺结构，并使用弦长中位数的体视学测量方法来表征其大小。我们在来自两种健康动物模型（猪和大鼠）的新鲜离体组织中，使用光学相干断层扫描针式探头获取的扫描图像上演示了该算法。将自动计算得出的肺结构大小估计值与手动测量值进行了验证。此外，我们使用为每个数据集计算的分割结果对肺结构进行了三维可视化。该方法有可能为包括慢性阻塞性肺疾病和肺纤维化在内的一系列呼吸系统疾病引起的结构重塑提供体内指标。
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