Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, Netherlands. Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, People's Republic of China.
Phys Med Biol. 2018 Aug 6;63(15):155024. doi: 10.1088/1361-6560/aad2a1.
Small airway obstruction is a main cause for chronic obstructive pulmonary disease (COPD). We propose a novel method based on machine learning to extract the airway system from a thoracic computed tomography (CT) scan. The emphasis of the proposed method is on including the smallest airways that are still visible on CT. We used an optimized sampling procedure to extract airway and non-airway voxel samples from a large set of scans for which a semi-automatically constructed reference standard was available. We created a set of features which represent tubular and texture properties that are characteristic for small airway voxels. A random forest classifier was used to determine for each voxel if it belongs to the airway class. Our method was validated on a set of 20 clinical thoracic CT scans from the COPDGene study. Experiments show that our method is effective in extracting the full airway system and in detecting a large number of small airways that were missed by the semi-automatically constructed reference standard.
小气道阻塞是慢性阻塞性肺疾病（COPD）的主要原因。我们提出了一种基于机器学习的新方法，用于从胸部计算机断层扫描（CT）中提取气道系统。该方法的重点是包括在 CT 上仍可见的最小气道。我们使用优化的采样程序，从一组大型扫描中提取气道和非气道体素样本，这些扫描具有半自动构建的参考标准。我们创建了一组特征，这些特征代表了小气道体素特有的管状和纹理特性。随机森林分类器用于确定每个体素是否属于气道类。我们的方法在 COPDGene 研究中的 20 例临床胸部 CT 扫描中进行了验证。实验表明，我们的方法可以有效地提取整个气道系统，并检测到大量半自动构建的参考标准错过的小气道。
