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首页> 外文会议>SPIE Conference on Biomedical Applications in Molecular, Structural, and Functional Imaging >Automated segmentation of lung airway wall area measurements frombronchoscopic Optical Coherence Tomography imaging
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Automated segmentation of lung airway wall area measurements frombronchoscopic Optical Coherence Tomography imaging

机译：肺气道墙面积的自动分割伯戈伦奇光学相干断层摄影成像成像

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Chronic Obstructive Pulmonary Disease (COPD) affects almost 600 million people and is currently the fourth leading cause of death worldwide. COPD is an umbrella term for respiratory symptoms that accompany destruction of the lung parenchyma and/or remodeling of the airway wall, the sum of which result in decreased expiratory flow, dyspnea and gas trapping. Currently, x-ray computed tomography (CT) is the main clinical method used for COPD imaging, providing excellent spatial resolution for quantitative tissue measurements although dose limitations and the fundamental spatial resolution of CT limit the measurement of airway dimensions beyond the 5th generation. To address this limitation, we are piloting the use of bronchoscopic Optical Coherence Tomography (OCT), by exploiting its superior spatial resolution of 5-15 micrometers for in vivo airway imaging. Currently, only manual segmentation of OCT airway lumen and wall have been reported but manual methods are time consuming and prone to observer variability. To expand the utility of bronchoscopic OCT, automatic and robust measurement methods are required. Therefore, our objective was to develop a fully automated method for segmenting OCT airway wall dimensions and here we explore several different methods of image-regeneration, voxel clustering and post-processing. Our resultant automated method used K-means or Fuzzy c-means to cluster pixel intensity and then a series of algorithms (i.e. cluster selection, artifact removal, de-noising) was applied to process the clustering results and segment airway wall dimensions. This approach provides a way to automatically and rapidly segment and reproducibly measure airway lumen and wall area.
机译：慢性阻塞性肺病（COPD）影响着近600万人口，是目前全世界死亡的第四大原因。 COPD是伴随肺实质的破坏和/或气道壁的重塑呼吸道症状的总称，其导致减少总和呼气流量，呼吸困难和气体截留。目前，X射线计算机断层摄影（CT）是用于COPD成像的主要临床方法，提供用于定量组织测量优良的空间分辨率虽然剂量限制和CT的基本空间分辨率限制超出了第五代气道的尺寸的测量。为了解决此限制，我们正试用使用支气管镜光学相干断层扫描（OCT）的，通过利用5-15微米其优越的空间分辨率用于体内气道成像。目前，只有华侨城气道腔内的手动分割和墙面已被报道，但手工方法既费时又容易观察员变异。为了扩大支气管镜OCT的效用，需要自动和鲁棒的测量方法。因此，我们的目标是开发出分割OCT气道壁尺寸的全自动的方法，在这里我们探讨图像再生，体素聚类和后处理的几种不同的方法。我们得到的自动化方法，使用K均值或模糊C均值聚类到像素强度，然后一系列算法（即群集选择，假象去除，去噪）施加到处理聚类结果和段气道壁的尺寸。这种方法提供了一种方法来自动地和迅速段和可重复测量气道腔和壁面积。

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