Airway anatomy as a risk factor of COPD

Bokov, Plamen; Delclaux, Christophe

doi:10.1136/thoraxjnl-2014-206534

Cited by 1 publication

(2 citation statements)

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“…Previous reports have asserted that these abnormalities in small airways will be reflected in large-and intermediate-sized airways [45]- [47]. A reduced tracheal section, total airway count on CT and airway branch variation have been proposed as the independent and innate risk factor of COPD [17], [19], [20], [43]. Our study has provided further evidence to the above rationale.…”

Section: A Pathophysiological Rationale Of Airway Abnormalities In Copdsupporting

confidence: 75%

“…The abnormalities in COPD airway can be thicker wall thickness, smaller airway intraluminal area (Ai) of third-to sixth-generation bronchi [16], more branch variation [17], smaller child-to-parent diameter ratios, larger length-to-diameter ratios, and smaller fractal dimensions [18]. They can also be Pi10 [13], tracheal section [19], CT total airway count [20], and tracheal shape [21].…”

Section: Introductionmentioning

confidence: 99%

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Identification of COPD From Multi-View Snapshots of 3D Lung Airway Tree via Deep CNN

Feng³

et al. 2020

IEEE Access

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Chronic obstructive pulmonary disease (COPD) is associated with morphologic abnormalities of airways with various patterns and severities. However, the way of effectively representing these abnormalities is lacking and whether these abnormalities enable to distinguish COPD from healthy controls is unknown. We propose to use deep convolutional neural network (CNN) to assess 3D lung airway tree from the perspective of computer vision, thereby constructing models of identifying COPD. After extracting airway trees from CT images, snapshots of their 3D visualizations are obtained from ventral, dorsal and isometric views. Using snapshots of each view, one deep CNN model is constructed and further optimized by Bayesian optimization algorithm to indentify COPD. The majority voting of three views presents the final prediction. Finally, the class-discriminative localization maps have been drawn to visually explain the CNNs' decisions. The models trained with single view (ventral, dorsal and isometric) of colorful snapshots present the similar accuracy (ACC) (86.8%, 87.5% and 86.7%) and the model after voting achieves the ACC of 88.2%. The ACC of the final voting model using gray and binary snapshots achieves 88.6% and 86.4%, respectively. Our specially designed CNNs outperform the typical off-the-shelf CNNs and the pre-trained CNNs with fine tuning. The class-discriminative regions of COPD are mainly located at central airways; however, regions in HC are scattering and located at peripheral airways. It is feasible to identify COPD using snapshots of 3D lung airway tree extracted from CT images via deep CNN. The CNNs can represent the abnormalities of airway tree in COPD and make accurate CT-based diagnosis of COPD. INDEX TERMS Chronic obstructive pulmonary disease (COPD), deep learning, convolutional neural networks, computed tomography (CT), airway, image classification.

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