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.
Osteoporosis is an age-related disease associated with reduced bone density and increased fracture-risk. It is known that bone microstructural quality is a significant determinant of trabecular bone strength and fracture-risk. Emerging CT technology allows high-resolution in vivo imaging at peripheral sites enabling assessment of bone microstructure at low radiation. Resolution dependence of bone microstructural measures together with varying technologies and rapid upgrades in CT scanners warrants data-harmonization in multi-site as well as longitudinal studies. This paper presents an unsupervised deep learning method for high-resolution reconstruction of bone microstructure from low-resolution CT scans using GAN-CIRCLE. The unsupervised training alleviates the need of registered low- and high-resolution images, which is often unavailable. Low- and high-resolution ankle CT scans of twenty volunteers were used for training, validation, and evaluation. Ten thousand unregistered low- and high-resolution patches of size 64×64 were randomly harvested from CT scans of ten volunteers for training and validation. Five thousand matched pairs of low- and highresolution patches were generated for evaluation after registering CT scan pairs from other ten volunteers. Quantitative comparison shows that predicted high-resolution scans have significantly improved structural similarity index (p < 0.01) with true high-resolution scans as compared to the same metric derived from low-resolution data. Also, trabecular bone microstructural measures such as thickness and network area measures computed from predicted high-resolution CT images showed higher (CCC = [0.90, 0.84]) agreement with the reference measures from true high-resolution scans compared to the same measures derived from low-resolution images (CCC = [0.66, 0.83]).
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%.
Osteoporosis is a common age-related disease characterized by reduced bone density and increased fracture-risk. Microstructural quality of trabecular bone (Tb), commonly found at axial skeletal sites and at the end of long bones, is an important determinant of bone-strength and fracture-risk. High-resolution emerging CT scanners enable in vivo measurement of Tb microstructures at peripheral sites. However, resolution-dependence of microstructural measures and wide resolution-discrepancies among various CT scanners together with rapid upgrades in technology warrant data harmonization in CT-based cross-sectional and longitudinal bone studies. This paper presents a deep learning-based method for high-resolution reconstruction of Tb microstructures from low-resolution CT scans using GAN-CIRCLE. A network was developed and evaluated using post-registered ankle CT scans of nineteen volunteers on both low- and highresolution CT scanners. 9,000 matching pairs of low- and high-resolution patches of size 64×64 were randomly harvested from ten volunteers for training and validation. Another 5,000 matching pairs of patches from nine other volunteers were used for evaluation. Quantitative comparison shows that predicted high-resolution scans have significantly improved structural similarity index (p < 0.01) with true high-resolution scans as compared to the same metric for low-resolution data. Different Tb microstructural measures such as thickness, spacing, and network area density are also computed from low- and predicted high-resolution images, and compared with the values derived from true high-resolution scans. Thickness and network area measures from predicted images showed higher agreement with true high-resolution CT (CCC = [0.95, 0.91]) derived values than the same measures from low-resolution images (CCC = [0.72, 0.88]).
Quantitative CT-based characterization of bronchial morphology is widely used in chronic obstructive pulmonary disease (COPD) related research and clinical studies. There are no fully automated airway tree segmentation methods, which is critical for large multi-site COPD studies. A critical challenge is that airway segmentation failures, e.g., leakages or early truncation, in even a small fraction of cases warrants manual intervention for all cases. In this paper, we present a fullyautomated CT-based hybrid algorithm for human airway segmentation that combines both deep learning and conventional image processing approaches. A three-dimensional (3-D) U-Net is developed to compute a voxel-level likelihood map of airway lumen space from a chest CT image at total lung capacity (TLC). This likelihood map is fed into a conventional image processing cascade that iteratively augments airway branches and removes leakages using newly developed freezeand-grow and progressive threshold parameter relaxation approaches. The new method has been applied on fifteen TLC human chest CT scans from an ongoing COPD Study and its performance has been quantitatively compared with the results of a semi-automated industry-standard software involving manual review and correction. Experimental results show significant improvements in terms of branch level accuracy using the new method as compared to the unedited results from the industry-standard method, while matching with their manually edited results. In terms of segmentation volume leakage, the new method significantly reduced segmentation leakages as compared to both unedited and edited results of the industry-standard method.
Numerous large multi-center studies are incorporating the use of computed tomography (CT)-based characterization of the lung parenchyma and bronchial tree to understand chronic obstructive pulmonary disease status and progression. To the best of our knowledge, there are no fully automated airway tree segmentation methods, free of the need for user review. A failure in even a fraction of segmentation results necessitates manual revision of all segmentation masks which is laborious considering the thousands of image data sets evaluated in large studies. In this paper, we present a novel CT-based airway tree segmentation algorithm using topological leakage detection and freeze-and-grow propagation. The method is fully automated requiring no manual inputs or post-segmentation editing. It uses simple intensity-based connectivity and a freeze-and-grow propagation algorithm to iteratively grow the airway tree starting from an initial seed inside the trachea. It begins with a conservative parameter and then, gradually shifts toward more generous parameter values. The method was applied on chest CT scans of fifteen subjects at total lung capacity. Airway segmentation results were qualitatively assessed and performed comparably to established airway segmentation method with no major visual leakages.
There are growing applications of quantitative computed tomography for assessment of pulmonary diseases by
characterizing lung parenchyma as well as the bronchial tree. Many large multi-center studies incorporating lung
imaging as a study component are interested in phenotypes relating airway branching patterns, wall-thickness, and other
morphological measures. To our knowledge, there are no fully automated airway tree segmentation methods, free of the
need for user review. Even when there are failures in a small fraction of segmentation results, the airway tree masks must
be manually reviewed for all results which is laborious considering that several thousands of image data sets are
evaluated in large studies. In this paper, we present a CT-based novel airway tree segmentation algorithm using iterative
multi-scale leakage detection, freezing, and active seed detection. The method is fully automated requiring no manual
inputs or post-segmentation editing. It uses simple intensity based connectivity and a new leakage detection algorithm to
iteratively grow an airway tree starting from an initial seed inside the trachea. It begins with a conservative threshold
and then, iteratively shifts toward generous values. The method was applied on chest CT scans of ten non-smoking
subjects at total lung capacity and ten at functional residual capacity. Airway segmentation results were compared to an
expert’s manually edited segmentations. Branch level accuracy of the new segmentation method was examined along
five standardized segmental airway paths (RB1, RB4, RB10, LB1, LB10) and two generations beyond these branches.
The method successfully detected all branches up to two generations beyond these segmental bronchi with no visual
leakages.
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