Anatomical Labeling of Human Airway Branches using a Novel Two-Step Machine Learning and Hierarchical Features.
Anatomical Labeling of Human Airway Branches using a Novel Two-Step Machine Learning and Hierarchical Features.
复制标题
使用新颖的两步机器学习和分层特征对人体气道分支进行解剖标记。
DOI：
10.1117/12.2546004
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Saha,PunamK
中科院分区：
文献类型：
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作者：
Nadeem,SyedAhmed;Hoffman,EricA;Comellas,AlejandroP;Saha,PunamK
慢性阻塞性肺疾病（COPD）是一种常见的与肺气流受限相关的炎症性疾病。基于定量计算机断层扫描（CT）的支气管测量广泛用于COPD相关研究，这需要气道分割和解剖分支标记。本文提出了一种基于两步机器学习和层次特征的人体气道树分支解剖标记算法。气道分支的解剖标记允许在基于大人群的研究中标准化气道表型的空间参考。最先进的解剖标记方法与强制性手动审查和纠正错误标记的分支相关-这是一个耗时的过程，易受观察者间差异的影响。新方法是完全自动化的，它使用当前分支以及祖先和后代分支的分层分支级别特征。在第一个机器学习步骤中，它将候选解剖分支与不重要的拓扑分支区分开来，这些拓扑分支通常导致气道分支模式的变化。第二步设计用于有效候选分支的解剖标签的基于肺叶的分类。机器学习分类器已被设计、训练并使用来自全国COPDGene研究的爱荷华州队列的总肺容量（TLC）CT扫描（n = 350）在其基线访视期间进行验证。使用100个TLC CT扫描进行训练和验证，并使用不同的250个扫描集进行测试和评价实验。新方法在右上、右中、右下、左上和左下叶的标记准确率分别为98.4%、97.2%、92.3%、93.4%和94.1%，总体准确率为95.9%。对于五个临床上有意义的节段性分支，该方法已达到95.2%的准确性。
Chronic obstructive pulmonary disease (COPD) is a common inflammatory disease associated with restricted lung airflow. Quantitative computed tomography (CT)-based bronchial measures are popularly used in COPD-related studies, which require both airway segmentation and anatomical branch labeling. This paper presents an algorithm for anatomical labeling of human airway tree branches using a novel two-step machine learning and hierarchical features. Anatomical labeling of airway branches allows standardized spatial referencing of airway phenotypes in large population-based studies. State-ofthe-art anatomical labeling methods are associated with mandatory manual reviewing and correction for mislabeled branches—a time-consuming process susceptible to inter-observer variability. The new method is fully automated, and it uses hierarchical branch-level features from the current as well as ancestral and descendant branches. During the first machine learning step, it differentiates candidate anatomical branches from insignificant topological branches, often, responsible for variations in airway branching patterns. The second step is designed for lung lobe-based classification of anatomical labels for valid candidate branches. The machine learning classifiers has been designed, trained, and validated using total lung capacity (TLC) CT scans (n = 350) from the Iowa cohort of the nationwide COPDGene study during their baseline visits. One hundred TLC CT scans were used for training and validation, and a different set of 250 scans were used for testing and evaluative experiments. The new method achieved labeling accuracies of 98.4, 97.2, 92.3, 93.4, and 94.1% in the right upper, right middle, right lower, left upper, and left lower lobe, respectively, and an overall accuracy of 95.9%. For five clinically significant segmental branches, the method has achieved an accuracy of 95.2%.