Aging of the thoracic musculoskeletal system can result in adverse impacts on lung function. Measurement of rib morphology in chest CT scans and assessment of their changes between full inspiratory, or total lung capacity (TLC), and full expiratory, or residual volume (RV), help examine the impacts of rib cage-related changes on lung function. We present new and automated methods using deep learning, multi-parametric thresholding, and topological analysis to segment and label individual ribs in chest CT scans, compute static morphological features at individual rib locations, and assess their lung volume related changes (ΔLV) between TLC and RV scans. The method was applied on TLC and RV scans from the COPDGene Iowa cohort at baseline visits, and accuracy of rib segmentation and computed metrics were examined by comparing with manually outlined results on TLC and RV scans (n=2×20). An average Dice score of 0.93 was observed in all TLC and RV rib segmentations, and root-mean-square errors for different static and ΔLV metrics were found between 0.7 and 4.9%. Application on a larger population (n=200) revealed a five-year loss of 6.2% (p<.001) in the trendline for ΔLV in the anterior-posterior diameter of the 5th rib with losses of 8.4 and 4.0% for males and females, respectively. Automation of CT-based static and ΔLV metrics of rib morphology and significant evidence of age-related changes and sex-bias establish a novel and effective tool to investigate the influence of different risk factors and comorbidities in patients with chronic lung disease and their impacts on disease progression and clinical outcomes.
Deterioration of the overall musculoskeletal system with aging is a universal phenomenon influenced by different demographic and lifestyle factors. Often, pectoral muscle metrics are used to describe overall muscle health, and CTbased studies have demonstrated their associations with various diseases, lung function, and mortality. However, these studies use extremely laborious manual means to segment pectoral muscles limiting both study size and scope. Here, we present a CT-based automated method for segmentation of the pectoral muscle using deep learning and computation of pectoral muscle area (PMA). We examined the extent of change in PMA with aging and sex using retrospective chest CT scans (n = 260) from COPDGene Iowa cohort at baseline visits. A two-dimensional U-Net was developed, optimized, and trained (n = 60) to generate a pixel-wise pectoral muscle probability map from chest CT scans, which was followed by an image post-processing cascade to segment the muscle area. Preliminary results (n = 200) show that our CT-based automated segmentation method is accurate (Dice score = 0.93), and it detects muscle wasting with aging. Males had significantly greater PMA as compared to females (effect size: 0.84; p < 0.001). A five-year loss in PMA of 4.8% was observed in the study population with losses of 4.3% and 5.1% for females and males, respectively. Chest CT-based automated methods for pectoral muscle segmentation are suitable for large population studies exploring broader scientific knowledge under various diseases.
Spinal degeneration and vertebral fractures are common among the elderly adversely impacting mobility, quality of life, lung function, fracture risk, and mortality. Segmentation of individual vertebrae from computed tomography (CT) imaging is crucial for studying spine degeneration, vertebral fractures, and bone density with aging and their mechanistic links with demographics, lifestyle factors, and comorbidities. We present an automated method to segment individual vertebral bodies (T1-L1) and compute the kyphotic angle of the spine from chest CT images. A three-dimensional U-Net was developed, optimized, and trained to generate a voxellevel vertebral probability map from a chest CT scan. Multi-parametric thresholding was applied on the probability map to segment individual vertebrae by iteratively relaxing the probability threshold value, while avoiding fusion among adjacent vertebrae. The kyphotic angle was computed using two orthogonal planes on the spine centerline at the inter-vertebral spaces T3-T4 and T12-L1 and a common sagittal plane. Total lung capacity (TLC) chest CT scans from baseline visits of the COPDGene Iowa cohort were used for our experiments. The U-Net method was trained and validated using 40 scans and tested on a separate set of 100 scans. Segmentation of individual vertebrae achieved a mean Dice score of 0.93 as compared to manual segmentation, and the kyphotic angle computation method produced a linear correlation of 0.88 (r-value) with manual measurements. This method provides a fully automated tool to study different mechanistic pathways of age-related spine modeling and vertebral fractures in retrospective datasets available from large multi-site chest related studies.
Chronic obstructive pulmonary disease (COPD) is a common inflammatory disease characterized by restricted lung airflow affecting over 300 million people worldwide. Quantitative computed tomography (CT) has become a benchmark for large multi-center pulmonary research studies for assessment of airway and parenchymal physiology and function towards understanding the occurrence and progression of the disease. Airway tree segmentation is a precursor for such approaches; but current industry-standard methods require manual post-segmentation correction to remove leakages and add missing airway branches. Recently, deep learning (DL) methods have gained popularity in medical image segmentation and outperformed traditional image processing methods due to their data-driven optimization schemes of multi-layered and multi-scale features. Generalizability of DL methods is a lingering concern and essential in multi-site CT-based pulmonary studies due to varying CT imaging settings at different sites. In this paper, we examine the generalizability of a recently developed fully automated DL-based airway segmentation method using low-dose chest CT images from the NELSON lung cancer screening study. The DL method was trained using high-dose chest CT scans from the Iowa cohort of COPDGene study at baseline visits and applied on blinded low-dose images. Results show the recent DL-based method is generalizable to blinded low-dose chest CT imaging, and it achieves branch-level accuracies of 100, 99.6, and 96.0% at segmental, sub-segmental, and sub-sub-segmental branches along the five clinically significant bronchial paths (RB1, RB4, RB10, LB1, and LB10).
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%.
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