Airway Detection in COPD at Low-Dose CT Using Deep Learning and Multiparametric Freeze and Grow

Automated CT-based measurements of radial and longitudinal expansion of airways due to breathing-related lung volume change

BACKGROUND

Respiratory function is impaired in chronic obstructive pulmonary disease (COPD). Automation of multi-volume CT-based measurements of different components of breathing-related airway deformations will help understand multi-pathway impairments in respiratory mechanics in COPD.

PURPOSE

To develop and evaluate multi-volume chest CT-based automated measurements of breathing-related radial and longitudinal expansion of individual airways between inspiratory and expiratory lung volumes.

METHODS

We developed a method to compute breathing-related airway deformation metrics and applied it to total lung capacity (TLC) and functional residual capacity (FRC) chest CT scans. The computational pipeline involves: (1) segmentation of airways; (2) skeletonization of airways; (3) labeling of anatomical airway segments at TLC and FRC; and (4) computation of radial and longitudinal expansion metrics of individual airways across lung volumes. Radial expansion (∆CSA) of an airway is computed as the percent change of its cross-sectional area (CSA) between two lung volumes. Longitudinal expansion (∆L) of an airway is computed as the percent change in its airway path-length from the carina between lung volumes. These measures are summarized at different airway anatomic generations. Agreement of automated measures with their manually derived values was examined in terms of concordance correlation coefficient (CCC) of automated measures with those derived using manual outlining. Intra-class correlation coefficient (ICC) of automated measures from repeat CT scans (n = 37) was computed to assess repeatability. The method was also applied to a set of participants from the Genetic Epidemiology of COPD (COPDGene) Iowa cohort, distributed across COPD severity groups (n = 4 × 60).

RESULTS

The CCC values for the automated ∆CSA measure with manually derived values were 0.930 at the trachea, 0.898 at primary bronchi, and greater than 0.95 at pre-segmental and segmental airways; these CCC values were consistently greater than 0.95 for ∆L at all airway generations. ICC values for repeatability of ∆CSA were 0.974, 0.950, 0.943, and 0.901 at trachea, primary bronchi, pre-segmental, and segmental airways, respectively; these ICC values for ∆L were 0.973, 0.954, and 0.952 at primary bronchi, pre-segmental, and segmental airways, respectively. ∆CSA values were significantly reduced (p < 0.001) with increasing COPD severity at each of primary bronchi, pre-segmental, and segmental airways. Significantly lower ∆L values were observed for moderate (p = 0.042 at pre-segmental and p = 0.037 at segmental) and severe (p = 0.019 at pre-segmental and p < 0.001 at segmental) COPD groups as compared to the preserved lung function group. Body mass index (BMI) and smoking status were found to significantly associate with ∆CSA at segmental airways (r = 0.17 and -0.19, respectively; significance threshold = 0.13), while age and sex were significantly associated with ∆L (r = -0.21 and -0.17, respectively); COPD severity was significantly associated with both ∆CSA and ∆L (r = -0.35 and -0.22, respectively).

CONCLUSION

Our CT-based automated measures of breathing-related radial and longitudinal expansion of airways are repeatable and in agreement with manually derived values. Automation of different airway mechanical biomarkers and their observed significant associations with age, sex, BMI, smoking, and COPD severity establish an effective tool to investigate multi-pathway impairments of respiratory mechanics in COPD and other lung diseases.

Automated CT-based decoupling of the effects of airway narrowing and wall thinning on airway counts in chronic obstructive pulmonary disease

OBJECTIVE

We examine pathways of airway alteration due to wall thinning, narrowing, and obliteration in chronic obstructive pulmonary disease (COPD) using CT-derived airway metrics.

METHODS

Ex-smokers (N = 649; age mean ± std: 69 ± 6 years; 52% male) from the COPDGene Iowa cohort (September 2013-July 2017) were studied. Total airway count (TAC), peripheral TAC beyond 7th generation (TACp), and airway wall thickness (WT) were computed from chest CT scans using previously validated automated methods. Causal relationships among demographic, smoking, spirometry, COPD severity, airway counts, WT, and scanner variables were analysed using causal inference techniques including direct acyclic graphs to assess multi-pathway alterations of airways in COPD.

RESULTS

TAC, TACp, and WT were significantly lower (P < .0001) in mild, moderate, and severe COPD compared to the preserved lung function group. TAC (TACp) losses attributed to narrowing and obliteration of small airways were 4.59%, 13.29%, and 32.58% (4.64%, 17.82%, and 45.51%) in mild, moderate, and severe COPD, while the losses attributed to wall thinning were 8.24%, 17.01%, and 22.95% (12.79%, 25.66%, and 33.95%) in respective groups.

CONCLUSIONS

Different pathways of airway alteration in COPD are observed using CT-derived automated airway metrics. Wall thinning is a dominant contributor to both TAC and TACp loss in mild and moderate COPD while narrowing and obliteration of small airways is dominant in severe COPD.

ADVANCES IN KNOWLEDGE

This automated CT-based study shows that wall thinning dominates airway alteration in mild and moderate COPD while narrowing and obliteration of small airways leads the alteration process in severe COPD.

Advancements in automated classification of chronic obstructive pulmonary disease based on computed tomography imaging features through deep learning approaches

Chronic Obstructive Pulmonary Disease (COPD) represents a global public health issue that significantly impairs patients' quality of life and overall health. As one of the primary causes of chronic respiratory diseases and global mortality, effective diagnosis and classification of COPD are crucial for clinical management. Pulmonary function tests (PFTs) are standard for diagnosing COPD, yet their accuracy is influenced by patient compliance and other factors, and they struggle to detect early disease pathologies. Furthermore, the complexity of COPD pathological changes poses additional challenges for clinical diagnosis, increasing the difficulty for physicians in practice. Recently, deep learning (DL) technologies have demonstrated significant potential in medical image analysis, particularly for the diagnosis and classification of COPD. By analyzing key radiological features such as airway alterations, emphysema, and vascular characteristics in Computed Tomography (CT) scan images, DL enhances diagnostic accuracy and efficiency, providing more precise treatment plans for COPD patients. This article reviews the latest research advancements in DL methods based on principal radiological features of COPD for its classification and discusses the advantages, challenges, and future research directions of DL in this field, aiming to provide new perspectives for the personalized management and treatment of COPD.

Chest CT-based automated vertebral fracture assessment using artificial intelligence and morphologic features

BACKGROUND

Spinal degeneration and vertebral compression fractures are common among the elderly that adversely affect their mobility, quality of life, lung function, and mortality. Assessment of vertebral fractures in chronic obstructive pulmonary disease (COPD) is important due to the high prevalence of osteoporosis and associated vertebral fractures in COPD.

PURPOSE

We present new automated methods for (1) segmentation and labelling of individual vertebrae in chest computed tomography (CT) images using deep learning (DL), multi-parametric freeze-and-grow (FG) algorithm, and separation of apparently fused vertebrae using intensity autocorrelation and (2) vertebral deformity fracture detection using computed vertebral height features and parametric computational modelling of an established protocol outlined for trained human experts.

METHODS

A chest CT-based automated method was developed for quantitative deformity fracture assessment following the protocol by Genant et al. The computational method was accomplished in the following steps: (1) computation of a voxel-level vertebral body likelihood map from chest CT using a trained DL network; (2) delineation and labelling of individual vertebrae on the likelihood map using an iterative multi-parametric FG algorithm; (3) separation of apparently fused vertebrae in CT using intensity autocorrelation; (4) computation of vertebral heights using contour analysis on the central anterior-posterior (AP) plane of a vertebral body; (5) assessment of vertebral fracture status using ratio functions of vertebral heights and optimized thresholds. The method was applied to inspiratory or total lung capacity (TLC) chest scans from the multi-site Genetic Epidemiology of COPD (COPDGene) (ClinicalTrials.gov: NCT00608764) study, and the performance was examined (n = 3231). One hundred and twenty scans randomly selected from this dataset were partitioned into training (n = 80) and validation (n = 40) datasets for the DL-based vertebral body classifier. Also, generalizability of the method to low dose CT imaging (n = 236) was evaluated.

RESULTS

The vertebral segmentation module achieved a Dice score of .984 as compared to manual outlining results as reference (n = 100); the segmentation performance was consistent across images with the minimum and maximum of Dice scores among images being .980 and .989, respectively. The vertebral labelling module achieved 100% accuracy (n = 100). For low dose CT, the segmentation module produced image-level minimum and maximum Dice scores of .995 and .999, respectively, as compared to standard dose CT as the reference; vertebral labelling at low dose CT was fully consistent with standard dose CT (n = 236). The fracture assessment method achieved overall accuracy, sensitivity, and specificity of 98.3%, 94.8%, and 98.5%, respectively, for 40,050 vertebrae from 3231 COPDGene participants. For generalizability experiments, fracture assessment from low dose CT was consistent with the reference standard dose CT results across all participants.

CONCLUSIONS

Our CT-based automated method for vertebral fracture assessment is accurate, and it offers a feasible alternative to manual expert reading, especially for large population-based studies, where automation is important for high efficiency. Generalizability of the method to low dose CT imaging further extends the scope of application of the method, particularly since the usage of low dose CT imaging in large population-based studies has increased to reduce cumulative radiation exposure.

Advances in COPD imaging using CT and MRI: linkage with lung physiology and clinical outcomes

Recent years have witnessed major advances in lung imaging in patients with COPD. These include significant refinements in images obtained by computed tomography (CT) scans together with the introduction of new techniques and software that aim for obtaining the best image whilst using the lowest possible radiation dose. Magnetic resonance imaging (MRI) has also emerged as a useful radiation-free tool in assessing structural and more importantly functional derangements in patients with well-established COPD and smokers without COPD, even before the existence of overt changes in resting physiological lung function tests. Together, CT and MRI now allow objective quantification and assessment of structural changes within the airways, lung parenchyma and pulmonary vessels. Furthermore, CT and MRI can now provide objective assessments of regional lung ventilation and perfusion, and multinuclear MRI provides further insight into gas exchange; this can help in structured decisions regarding treatment plans. These advances in chest imaging techniques have brought new insights into our understanding of disease pathophysiology and characterising different disease phenotypes. The present review discusses, in detail, the advances in lung imaging in patients with COPD and how structural and functional imaging are linked with common resting physiological tests and important clinical outcomes.

A Survey on Artificial Intelligence in Pulmonary Imaging

Over the last decade, deep learning (DL) has contributed a paradigm shift in computer vision and image recognition creating widespread opportunities of using artificial intelligence in research as well as industrial applications. DL has been extensively studied in medical imaging applications, including those related to pulmonary diseases. Chronic obstructive pulmonary disease, asthma, lung cancer, pneumonia, and, more recently, COVID-19 are common lung diseases affecting nearly 7.4% of world population. Pulmonary imaging has been widely investigated toward improving our understanding of disease etiologies and early diagnosis and assessment of disease progression and clinical outcomes. DL has been broadly applied to solve various pulmonary image processing challenges including classification, recognition, registration, and segmentation. This paper presents a survey of pulmonary diseases, roles of imaging in translational and clinical pulmonary research, and applications of different DL architectures and methods in pulmonary imaging with emphasis on DL-based segmentation of major pulmonary anatomies such as lung volumes, lung lobes, pulmonary vessels, and airways as well as thoracic musculoskeletal anatomies related to pulmonary diseases.