Matching and anatomical labeling of human airway tree

Structure and position-aware graph neural network for airway labeling

We present a novel graph-based approach for labeling the anatomical branches of a given airway tree segmentation. The proposed method formulates airway labeling as a branch classification problem in the airway tree graph, where branch features are extracted using convolutional neural networks and enriched using graph neural networks. Our graph neural network is structure-aware by having each node aggregate information from its local neighbors and position-aware by encoding node positions in the graph. We evaluated the proposed method on 220 airway trees from subjects with various severity stages of Chronic Obstructive Pulmonary Disease (COPD). The results demonstrate that our approach is computationally efficient and significantly improves branch classification performance than the baseline method. The overall average accuracy of our method reaches 91.18% for labeling 18 segmental airway branches, compared to 83.83% obtained by the standard CNN method and 87.37% obtained by the existing method. Furthermore, the reader study done on an additional set of 40 subjects shows that our algorithm performs comparably to human experts in labeling segmental-airways. We published our source code at https://github.com/DIAGNijmegen/spgnn. The proposed algorithm is also publicly available at https://grand-challenge.org/algorithms/airway-anatomical-labeling/.

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.

Quantitative Imaging Metrics for the Assessment of Pulmonary Pathophysiology: An Official American Thoracic Society and Fleischner Society Joint Workshop Report

Multiple thoracic imaging modalities have been developed to link structure to function in the diagnosis and monitoring of lung disease. Volumetric computed tomography (CT) renders three-dimensional maps of lung structures and may be combined with positron emission tomography (PET) to obtain dynamic physiological data. Magnetic resonance imaging (MRI) using ultrashort-echo time (UTE) sequences has improved signal detection from lung parenchyma; contrast agents are used to deduce airway function, ventilation-perfusion-diffusion, and mechanics. Proton MRI can measure regional ventilation-perfusion ratio. Quantitative imaging (QI)-derived endpoints have been developed to identify structure-function phenotypes, including air-blood-tissue volume partition, bronchovascular remodeling, emphysema, fibrosis, and textural patterns indicating architectural alteration. Coregistered landmarks on paired images obtained at different lung volumes are used to infer airway caliber, air trapping, gas and blood transport, compliance, and deformation. This document summarizes fundamental "good practice" stereological principles in QI study design and analysis; evaluates technical capabilities and limitations of common imaging modalities; and assesses major QI endpoints regarding underlying assumptions and limitations, ability to detect and stratify heterogeneous, overlapping pathophysiology, and monitor disease progression and therapeutic response, correlated with and complementary to, functional indices. The goal is to promote unbiased quantification and interpretation of in vivo imaging data, compare metrics obtained using different QI modalities to ensure accurate and reproducible metric derivation, and avoid misrepresentation of inferred physiological processes. The role of imaging-based computational modeling in advancing these goals is emphasized. Fundamental principles outlined herein are critical for all forms of QI irrespective of acquisition modality or disease entity.

TNN: Tree Neural Network for Airway Anatomical Labeling

Detailed anatomical labeling of bronchial trees extracted from CT images can be used as fine-grained maps for intra-operative navigation. To cater to the sparse distribution of airway voxels and large class imbalance in 3D image space, a graph-neural-network-based method is proposed to map branches to nodes in a graph space and assign anatomical labels down to subsegmental level. To address the inherent problem of overlapping distribution of positional and morphological features, especially for subsegmental categories, the proposed method focuses on the relative position between sibling subsegments which is fixed in most cases. The hierarchical nomenclature is represented by multi-level labeling and each category is associated with one or two subtrees in the graph. Hyperedges are used to extract the representation of subtrees while a hypergraph neural network is developed to encode their intrinsic relationship through hyperedge interaction. A filter module is further designed to guide feature aggregation between nodes and hyperedges. With the proposed method, the final accuracies for segmental and subsegmental node classification can achieve 93.6% and 82.0% respectively. The corresponding code is publicly available at https://github.com/haozheng-sjtu/airway-labeling.

Origins of and lessons from quantitative functional X-ray computed tomography of the lung

Functional CT of the lung has emerged from quantitative CT (qCT). Structural details extracted at multiple lung volumes offer indices of function. Additionally, single volumetric images, if acquired at standardized lung volumes and body posture, can be used to model function by employing such engineering techniques as computational fluid dynamics. With the emergence of multispectral CT imaging including dual energy from energy integrating CT scanners and multienergy binning using the newly released photon counting CT technology, function is tagged via use of contrast agents. Lung disease phenotypes have previously been lumped together by the limitations of spirometry and plethysmography. QCT and its functional embodiment have been imbedded into studies seeking to characterize chronic obstructive pulmonary disease, severe asthma, interstitial lung disease and more. Reductions in radiation dose by an order of magnitude or more have been achieved. At the same time, we have seen significant increases in spatial and density resolution along with methodologic validations of extracted metrics. Together, these have allowed attention to turn towards more mild forms of disease and younger populations. In early applications, clinical CT offered anatomic details of the lung. Functional CT offers regional measures of lung mechanics, the assessment of functional small airways disease, as well as regional ventilation-perfusion matching (V/Q) and more. This paper will focus on the use of quantitative/functional CT for the non-invasive exploration of dynamic three-dimensional functioning of the breathing lung and beating heart within the unique negative pressure intrathoracic environment of the closed chest.

Ferret models of alpha-1 antitrypsin deficiency develop lung and liver disease

Alpha-1 antitrypsin deficiency (AATD) is the most common genetic cause and risk factor for chronic obstructive pulmonary disease, but the field lacks a large-animal model that allows for longitudinal assessment of pulmonary function. We hypothesized that ferrets would model human AATD-related lung and hepatic disease. AAT-knockout (AAT-KO) and PiZZ (E342K, the most common mutation in humans) ferrets were generated and compared with matched controls using custom-designed flexiVent modules to perform pulmonary function tests, quantitative computed tomography (QCT), bronchoalveolar lavage (BAL) proteomics, and alveolar morphometry. Complete loss of AAT (AAT-KO) led to increased pulmonary compliance and expiratory airflow limitation, consistent with obstructive lung disease. QCT and morphometry confirmed emphysema and airspace enlargement, respectively. Pathway analysis of BAL proteomics data revealed inflammatory lung disease and impaired cellular migration. The PiZ mutation resulted in altered AAT protein folding in the liver, hepatic injury, and reduced plasma concentrations of AAT, and PiZZ ferrets developed obstructive lung disease. In summary, AAT-KO and PiZZ ferrets model the progressive obstructive pulmonary disease seen in AAT-deficient patients and may serve as a platform for preclinical testing of therapeutics including gene therapy.

Automated anatomical labeling of a topologically variant abdominal arterial system via probabilistic hypergraph matching

Automated anatomical vessel labeling of the abdominal arterial system is a crucial topic in medical image processing. One reason for this is the importance of the abdominal arterial system in the human body, and another is that such labeling is necessary for the related disease diagnoses, treatments and epidemiological population analyses. We define a hypergraph representation of the abdominal arterial system as a family tree model with a probabilistic hypergraph matching framework for automated vessel labeling. Then we treat the labelling problem as the convex optimization problem and solve it with the maximum a posteriori(MAP) combined the likelihood obtained by geometric labelling with the family tree topology-based knowledge. Geometrically, we utilize XGBoost ensemble learning with an intrinsic geometric feature importance analysis for branch-level labeling. In topology, the defined family tree model of the abdominal arterial system is transferred as a Markov chain model using a constrained traversal order method and further the Markov chain model is optimized by a hidden Markov model (HMM). The probability distribution of the target branches for each candidate anatomical name is predicted and effectively embedded in the HMM model. This approach is evaluated with the leave-one-out method on 37 clinical patients' abdominal arteries, and the average accuracy is 91.94%. The obtained results are better than those of the state-of-art method with an F1 score of 93.00% and a recall of 93.00%, as the proposed method simultaneously handles the anatomical structural variability and discriminates between the symmetric branches. It is demonstrated to be suitable for labelling branches of the abdominal arterial system and can also be extended to similar tubular organ networks, such as arterial or airway networks.

Graph matching survey for medical imaging: On the way to deep learning

The interest on graph matching has not stopped growing since the late seventies. The basic idea of graph matching consists of generating graph representations of different data or structures and compare those representations by searching correspondences between them. There are manifold techniques that have been developed to find those correspondences and the choice of one or another depends on the characteristics of the application of interest. These applications range from pattern recognition (e.g. biometric identification) to signal processing or artificial intelligence. One of the aspects that make graph matching so attractive is its ability to facilitate data analysis, and medical imaging is one of the fields that can benefit from this in a greater extent. The potential of graph matching to find similarities and differences between data acquired at different points in time shows its potential to improve diagnosis, follow-up of human diseases or any other of the clinical scenarios that require comparison between different datasets. In spite of the large amount of papers that were published in this field to the date there is no survey paper of graph matching for clinical applications. This survey aims to fill this gap.

The Emerging Role of Quantification of Imaging for Assessing the Severity and Disease Activity of Emphysema, Airway Disease, and Interstitial Lung Disease

There has been an explosion of use for quantitative image analysis in the setting of lung disease due to advances in acquisition protocols and postprocessing technology, including machine and deep learning. Despite the plethora of published papers, it is important to understand which approach has clinical validation and can be used in clinical practice. This paper provides an introduction to quantitative image analysis techniques being used in the investigation of lung disease and focusses on the techniques that have a reasonable clinical validation for being used in clinical trials and patient care.

A CT-Based Automated Algorithm for Airway Segmentation Using Freeze-and-Grow Propagation and Deep Learning

Chronic obstructive pulmonary disease (COPD) is a common lung disease, and quantitative CT-based bronchial phenotypes are of increasing interest as a means of exploring COPD sub-phenotypes, establishing disease progression, and evaluating intervention outcomes. Reliable, fully automated, and accurate segmentation of pulmonary airway trees is critical to such exploration. We present a novel approach of multi-parametric freeze-and-grow (FG) propagation which starts with a conservative segmentation parameter and captures finer details through iterative parameter relaxation. First, a CT intensity-based FG algorithm is developed and applied for airway tree segmentation. A more efficient version is produced using deep learning methods generating airway lumen likelihood maps from CT images, which are input to the FG algorithm. Both CT intensity- and deep learning-based algorithms are fully automated, and their performance, in terms of repeat scan reproducibility, accuracy, and leakages, is evaluated and compared with results from several state-of-the-art methods including an industry-standard one, where segmentation results were manually reviewed and corrected. Both new algorithms show a reproducibility of 95% or higher for total lung capacity (TLC) repeat CT scans. Experiments on TLC CT scans from different imaging sites at standard and low radiation dosages show that both new algorithms outperform the other methods in terms of leakages and branch-level accuracy. Considering the performance and execution times, the deep learning-based FG algorithm is a fully automated option for large multi-site studies.

Quantitative Analysis of the Cerebral Vasculature on Magnetic Resonance Angiography

The arterial connections in the Circle of Willis are a central source of collateral blood flow and play an important role in pathologies such as stroke and mental illness. Analysis of the Circle of Willis and its variants can shed light on optimal methods of diagnosis, treatment planning, surgery, and quantification of outcomes. We developed an automated, standardized, objective, and high-throughput approach for categorizing and quantifying the Circle of Willis vascular anatomy using magnetic resonance angiography images. This automated algorithm for processing of MRA images isolates and automatically identifies key features of the cerebral vasculature such as branching of the internal intracranial internal carotid artery and the basilar artery. Subsequently, physical features of the segments of the anterior cerebral artery were acquired on a sample and intra-patient comparisons were made. We demonstrate the feasibility of using our approach to automatically classify important structures of the Circle of Willis and extract biomarkers from cerebrovasculature. Automated image analysis can provide clinically-relevant vascular features such as aplastic arteries, stenosis, aneurysms, and vessel caliper for endovascular procedures. The developed algorithm could facilitate clinical studies by supporting high-throughput automated analysis of the cerebral vasculature.

Anatomical Labeling of Human Airway Branches using a Novel Two-Step Machine Learning and Hierarchical Features

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-of-the-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%.

Development of a quantitative intracranial vascular features extraction tool on 3D MRA using semiautomated open-curve active contour vessel tracing

PURPOSE

To develop a quantitative intracranial artery measurement technique to extract comprehensive artery features from time-of-flight MR angiography (MRA).

METHODS

By semiautomatically tracing arteries based on an open-curve active contour model in a graphical user interface, 12 basic morphometric features and 16 basic intensity features for each artery were identified. Arteries were then classified as one of 24 types using prediction from a probability model. Based on the anatomical structures, features were integrated within 34 vascular groups for regional features of vascular trees. Eight 3D MRA acquisitions with intracranial atherosclerosis were assessed to validate this technique.

RESULTS

Arterial tracings were validated by an experienced neuroradiologist who checked agreement at bifurcation and stenosis locations. This technique achieved 94% sensitivity and 85% positive predictive values (PPV) for bifurcations, and 85% sensitivity and PPV for stenosis. Up to 1,456 features, such as length, volume, and averaged signal intensity for each artery, as well as vascular group in each of the MRA images, could be extracted to comprehensively reflect characteristics, distribution, and connectivity of arteries. Length for the M1 segment of the middle cerebral artery extracted by this technique was compared with reviewer-measured results, and the intraclass correlation coefficient was 0.97.

CONCLUSION

A semiautomated quantitative method to trace, label, and measure intracranial arteries from 3D-MRA was developed and validated. This technique can be used to facilitate quantitative intracranial vascular research, such as studying cerebrovascular adaptation to aging and disease conditions. Magn Reson Med 79:3229-3238, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Assessment of airway response distribution and paradoxical airway dilation in mice during methacholine challenge

Detailed information on the distribution of airway diameters during bronchoconstriction in situ is required to understand the regional response of the lungs. Imaging studies using computed tomography (CT) have previously measured airway diameters and changes in response to bronchoconstricting agents, but the manual measurements used have severely limited the number of airways measured per subject. Hence, the detailed distribution and heterogeneity of airway responses are unknown. We have developed and applied dynamic imaging and advanced image-processing methods to quantify and compare hundreds of airways in vivo. The method, based on CT, was applied to house dust-mite-sensitized and control mice during intravenous methacholine (MCh) infusion. Airway diameters were measured pre- and post-MCh challenge, and the results compared demonstrate the distribution of airway response throughout the lungs during mechanical ventilation. Forced oscillation testing was used to measure the global response in lung mechanics. We found marked heterogeneity in the response, with paradoxical dilation of airways present at all airway sizes. The probability of paradoxical dilation decreased with decreasing baseline airway diameter and was not affected by pre-existing inflammation. The results confirm the importance of considering the lung as an entire interconnected system rather than a collection of independent units. It is hoped that the response distribution measurements can help to elucidate the mechanisms that lead to heterogeneous airway response in vivo.

NEW & NOTEWORTHY Information on the distribution of airway diameters during bronchoconstriction in situ is critical for understanding the regional response of the lungs. We have developed an imaging method to quantify and compare the size of hundreds of airways in vivo during bronchoconstriction in mice. The results demonstrate large heterogeneity with both constriction and paradoxical dilation of airways, confirming the importance of considering the lung as an interconnected system rather than a collection of independent units.

LINKING LUNG AIRWAY STRUCTURE TO PULMONARY FUNCTION VIA COMPOSITE BRIDGE REGRESSION

The human lung airway is a complex inverted tree-like structure. Detailed airway measurements can be extracted from MDCT-scanned lung images, such as segmental wall thickness, airway diameter, parent-child branch angles, etc. The wealth of lung airway data provides a unique opportunity for advancing our understanding of the fundamental structure-function relationships within the lung. An important problem is to construct and identify important lung airway features in normal subjects and connect these to standardized pulmonary function test results such as FEV1%. Among other things, the problem is complicated by the fact that a particular airway feature may be an important (relevant) predictor only when it pertains to segments of certain generations. Thus, the key is an efficient, consistent method for simultaneously conducting group selection (lung airway feature types) and within-group variable selection (airway generations), i.e., bi-level selection. Here we streamline a comprehensive procedure to process the lung airway data via imputation, normalization, transformation and groupwise principal component analysis, and then adopt a new composite penalized regression approach for conducting bi-level feature selection. As a prototype of composite penalization, the proposed composite bridge regression method is shown to admit an efficient algorithm, enjoy bi-level oracle properties, and outperform several existing methods. We analyze the MDCT lung image data from a cohort of 132 subjects with normal lung function. Our results show that, lung function in terms of FEV1% is promoted by having a less dense and more homogeneous lung comprising an airway whose segments enjoy more heterogeneity in wall thicknesses, larger mean diameters, lumen areas and branch angles. These data hold the potential of defining more accurately the "normal" subject population with borderline atypical lung functions that are clearly influenced by many genetic and environmental factors.

BlastNeuron for Automated Comparison, Retrieval and Clustering of 3D Neuron Morphologies

Characterizing the identity and types of neurons in the brain, as well as their associated function, requires a means of quantifying and comparing 3D neuron morphology. Presently, neuron comparison methods are based on statistics from neuronal morphology such as size and number of branches, which are not fully suitable for detecting local similarities and differences in the detailed structure. We developed BlastNeuron to compare neurons in terms of their global appearance, detailed arborization patterns, and topological similarity. BlastNeuron first compares and clusters 3D neuron reconstructions based on global morphology features and moment invariants, independent of their orientations, sizes, level of reconstruction and other variations. Subsequently, BlastNeuron performs local alignment between any pair of retrieved neurons via a tree-topology driven dynamic programming method. A 3D correspondence map can thus be generated at the resolution of single reconstruction nodes. We applied BlastNeuron to three datasets: (1) 10,000+ neuron reconstructions from a public morphology database, (2) 681 newly and manually reconstructed neurons, and (3) neurons reconstructions produced using several independent reconstruction methods. Our approach was able to accurately and efficiently retrieve morphologically and functionally similar neuron structures from large morphology database, identify the local common structures, and find clusters of neurons that share similarities in both morphology and molecular profiles.

Inexact coronary tree matching algorithm with artificial nodes

Coronary tree matching is applied to plan percutaneous vascular procedures. This work, which allows following each segment of non-isomorphic coronary trees over time, precedes the determination of the best 2D angiography view from C-arm acquisition system for angioplasty procedure. To match two 3D coronary trees which represent two successive cardiac phases, we adapted a reference inexact tree matching algorithm based on association graph and maximum clique. To improve the pair-wise matching performance of our approach, artificial nodes are introduced to take into account the topology variation between 3D vascular trees. Different similarity measures using tree characteristics and geometric features of coronary branches are evaluated and compared to our previous work.

A tree-matching algorithm: Application to airways in CT images of subjects with the acute respiratory distress syndrome

To match anatomical trees such as airways, we propose a graph-based strategy combined with an appropriate distance function. The strategy was devised to cope with topological and geometrical differences that may arise between trees corresponding to the same subject, but extracted from images acquired in different conditions. The proposed distance function, called father/family distance, combines topological and geometrical information in a single measure, by calculating a sum of path-to-path distances between sub-trees of limited extent. To use it successfully, the branches of these sub-trees need to be brought closer, which is obtained by successively translating the roots of these sub-trees prior to their actual matching. The work herein presented contributes to a study of the acute respiratory distress syndrome, where a series of pulmonary CT images from the same subject is acquired at varying settings (pressure and volume) of the mechanical ventilation. The method was evaluated on 45 combinations of synthetic trees, as well as on 15 pairs of real airway trees: nine corresponding to end-expiration and end-inspiration with the same pressure, and six corresponding to end-inspiration with significantly different pressures. It achieved a high rate of successful matches with respect to a hand-made reference containing a total of 2391 matches in real data: sensitivity of 94.3% and precision of 92.8%, when using the basic parameter settings of the algorithm.

Acute administration of ivacaftor to people with cystic fibrosis and a G551D-CFTR mutation reveals smooth muscle abnormalities

BACKGROUND

Airflow obstruction is common in cystic fibrosis (CF), yet the underlying pathogenesis remains incompletely understood. People with CF often exhibit airway hyperresponsiveness, CF transmembrane conductance regulator (CFTR) is present in airway smooth muscle (ASM), and ASM from newborn CF pigs has increased contractile tone, suggesting that loss of CFTR causes a primary defect in ASM function. We hypothesized that restoring CFTR activity would decrease smooth muscle tone in people with CF.

METHODS

To increase or potentiate CFTR function, we administered ivacaftor to 12 adults with CF with the G551D-CFTR mutation; ivacaftor stimulates G551D-CFTR function. We studied people before and immediately after initiation of ivacaftor (48 hours) to minimize secondary consequences of CFTR restoration. We tested smooth muscle function by investigating spirometry, airway distensibility, and vascular tone.

RESULTS

Ivacaftor rapidly restored CFTR function, indicated by reduced sweat chloride concentration. Airflow obstruction and air trapping also improved. Airway distensibility increased in airways less than 4.5 mm but not in larger-sized airways. To assess smooth muscle function in a tissue outside the lung, we measured vascular pulse wave velocity (PWV) and augmentation index, which both decreased following CFTR potentiation. Finally, change in distensibility of <4.5-mm airways correlated with changes in PWV.

CONCLUSIONS

Acute CFTR potentiation provided a unique opportunity to investigate CFTR-dependent mechanisms of CF pathogenesis. The rapid effects of ivacaftor on airway distensibility and vascular tone suggest that CFTR dysfunction may directly cause increased smooth muscle tone in people with CF and that ivacaftor may relax smooth muscle.

FUNDING

This work was funded in part from an unrestricted grant from the Vertex Investigator-Initiated Studies Program.

A semi-automatic framework of measuring pulmonary arterial metrics at anatomic airway locations using CT imaging

Pulmonary vascular dysfunction has been implicated in smoking-related susceptibility to emphysema. With the growing interest in characterizing arterial morphology for early evaluation of the vascular role in pulmonary diseases, there is an increasing need for the standardization of a framework for arterial morphological assessment at airway segmental levels. In this paper, we present an effective and robust semi-automatic framework to segment pulmonary arteries at different anatomic airway branches and measure their cross-sectional area (CSA). The method starts with user-specified endpoints of a target arterial segment through a custom-built graphical user interface. It then automatically detect the centerline joining the endpoints, determines the local structure orientation and computes the CSA along the centerline after filtering out the adjacent pulmonary structures, such as veins or airway walls. Several new techniques are presented, including collision-impact based cost function for centerline detection, radial sample-line based CSA computation, and outlier analysis of radial distance to subtract adjacent neighboring structures in the CSA measurement. The method was applied to repeat-scan pulmonary multirow detector CT (MDCT) images from ten healthy subjects (age: 21-48 Yrs, mean: 28.5 Yrs; 7 female) at functional residual capacity (FRC). The reproducibility of computed arterial CSA from four airway segmental regions in middle and lower lobes was analyzed. The overall repeat-scan intra-class correlation (ICC) of the computed CSA from all four airway regions in ten subjects was 96% with maximum ICC found at LB10 and RB4 regions.

Pulmonary CT and MRI phenotypes that help explain chronic pulmonary obstruction disease pathophysiology and outcomes

Pulmonary x-ray computed tomographic (CT) and magnetic resonance imaging (MRI) research and development has been motivated, in part, by the quest to subphenotype common chronic lung diseases such as chronic obstructive pulmonary disease (COPD). For thoracic CT and MRI, the main COPD research tools, disease biomarkers are being validated that go beyond anatomy and structure to include pulmonary functional measurements such as regional ventilation, perfusion, and inflammation. In addition, there has also been a drive to improve spatial and contrast resolution while at the same time reducing or eliminating radiation exposure. Therefore, this review focuses on our evolving understanding of patient-relevant and clinically important COPD endpoints and how current and emerging MRI and CT tools and measurements may be exploited for their identification, quantification, and utilization. Since reviews of the imaging physics of pulmonary CT and MRI and reviews of other COPD imaging methods were previously published and well-summarized, we focus on the current clinical challenges in COPD and the potential of newly emerging MR and CT imaging measurements to address them. Here we summarize MRI and CT imaging methods and their clinical translation for generating reproducible and sensitive measurements of COPD related to pulmonary ventilation and perfusion as well as parenchyma morphology. The key clinical problems in COPD provide an important framework in which pulmonary imaging needs to rapidly move in order to address the staggering burden, costs, as well as the mortality and morbidity associated with COPD.

Digital Topology and Geometry in Medical Imaging: A Survey

Digital topology and geometry refers to the use of topologic and geometric properties and features for images defined in digital grids. Such methods have been widely used in many medical imaging applications, including image segmentation, visualization, manipulation, interpolation, registration, surface-tracking, object representation, correction, quantitative morphometry etc. Digital topology and geometry play important roles in medical imaging research by enriching the scope of target outcomes and by adding strong theoretical foundations with enhanced stability, fidelity, and efficiency. This paper presents a comprehensive yet compact survey on results, principles, and insights of methods related to digital topology and geometry with strong emphasis on understanding their roles in various medical imaging applications. Specifically, this paper reviews methods related to distance analysis and path propagation, connectivity, surface-tracking, image segmentation, boundary and centerline detection, topology preservation and local topological properties, skeletonization, and object representation, correction, and quantitative morphometry. A common thread among the topics reviewed in this paper is that their theory and algorithms use the principle of digital path connectivity, path propagation, and neighborhood analysis.

Topology adaptive vessel network skeleton extraction with novel medialness measuring function

Vessel tree skeleton extraction is widely applied in vascular structure segmentation, however, conventional approaches often suffer from the adjacent interferences and poor topological adaptability. To avoid these problems, a robust, topology adaptive tree-like structure skeleton extraction framework is proposed in this paper. Specifically, to avoid the adjacent interferences, a local message passing procedure called Gaussian affinity voting (GAV) is proposed to realize adaptive scale-growing of vessel voxels. Then the medialness measuring function (MMF) based on GAV, namely GAV-MMF, is constructed to extract medialness patterns robustly. In order to improve topological adaptability, a level-set graph embedded with GAV-MMF is employed to build initial curve skeletons without any user interaction. Furthermore, the GAV-MMF is embedded in stretching open active contours (SOAC) to drive the initial curves to the expected location, maintaining smoothness and continuity. In addition, to provide an accurate and smooth final skeleton tree topology, topological checks and skeleton network reconfiguration is proposed. The continuity and scalability of this method is validated experimentally on synthetic and clinical images for multi-scale vessels. Experimental results show that the proposed method achieves acceptable topological adaptability for skeleton extraction of vessel trees.

Geodesic Atlas-Based Labeling of Anatomical Trees: Application and Evaluation on Airways Extracted From CT

We present a fast and robust atlas-based algorithm for labeling airway trees, using geodesic distances in a geometric tree-space. Possible branch label configurations for an unlabeled airway tree are evaluated using distances to a training set of labeled airway trees. In tree-space, airway tree topology and geometry change continuously, giving a natural automatic handling of anatomical differences and noise. A hierarchical approach makes the algorithm efficient, assigning labels from the trachea and downwards. Only the airway centerline tree is used, which is relatively unaffected by pathology. The algorithm is evaluated on 80 segmented airway trees from 40 subjects at two time points, labeled by three medical experts each, testing accuracy, reproducibility and robustness in patients with chronic obstructive pulmonary disease (COPD). The accuracy of the algorithm is statistically similar to that of the experts and not significantly correlated with COPD severity. The reproducibility of the algorithm is significantly better than that of the experts, and negatively correlated with COPD severity. Evaluation of the algorithm on a longitudinal set of 8724 trees from a lung cancer screening trial shows that the algorithm can be used in large scale studies with high reproducibility, and that the negative correlation of reproducibility with COPD severity can be explained by missing branches, for instance due to segmentation problems in COPD patients. We conclude that the algorithm is robust to COPD severity given equally complete airway trees, and comparable in performance to that of experts in pulmonary medicine, emphasizing the suitability of the labeling algorithm for clinical use.

A Robust and Efficient Curve Skeletonization Algorithm for Tree-Like Objects Using Minimum Cost Paths

Conventional curve skeletonization algorithms using the principle of Blum's transform, often, produce unwanted spurious branches due to boundary irregularities, digital effects, and other artifacts. This paper presents a new robust and efficient curve skeletonization algorithm for three-dimensional (3-D) elongated fuzzy objects using a minimum cost path approach, which avoids spurious branches without requiring post-pruning. Starting from a root voxel, the method iteratively expands the skeleton by adding new branches in each iteration that connects the farthest quench voxel to the current skeleton using a minimum cost path. The path-cost function is formulated using a novel measure of local significance factor defined by the fuzzy distance transform field, which forces the path to stick to the centerline of an object. The algorithm terminates when dilated skeletal branches fill the entire object volume or the current farthest quench voxel fails to generate a meaningful skeletal branch. Accuracy of the algorithm has been evaluated using computer-generated phantoms with known skeletons. Performance of the method in terms of false and missing skeletal branches, as defined by human experts, has been examined using in vivo CT imaging of human intrathoracic airways. Results from both experiments have established the superiority of the new method as compared to the existing methods in terms of accuracy as well as robustness in detecting true and false skeletal branches. The new algorithm makes a significant reduction in computation complexity by enabling detection of multiple new skeletal branches in one iteration. Specifically, this algorithm reduces the number of iterations from the number of terminal tree branches to the worst case performance of tree depth. In fact, experimental results suggest that, on an average, the order of computation complexity is reduced to the logarithm of the number of terminal branches of a tree-like object.

Relative contributions of emphysema and airway remodelling to airflow limitation in COPD: Consistent results from two cohorts

BACKGROUND AND OBJECTIVE

The relative contributions of emphysema and airway remodelling to airflow limitation remain unclear in chronic obstructive pulmonary disease (COPD). We aimed to evaluate the relative contributions of emphysema and airway wall thickness measured by quantitative computed tomography (CT) to the prediction of airflow limitation in two separate COPD cohorts.

METHODS

Pulmonary function tests and whole-lung CT were performed in 250 male smokers with COPD, including 167 from University Medical Center at Ho Chi Minh City, Vietnam, and 83 from Shiga University of Medical Science Hospital, Japan. The same CT analysis software was used to measure the percentage of low attenuation volume (%LAV) at the threshold of -950 Hounsfield units and the square root of wall area of a hypothetical airway with an internal perimeter of 10 mm (Pi10). The standardized coefficients in multiple linear regressions were used to evaluate the relative contributions of %LAV and Pi10 to predictions of FEV1 /FVC and FEV1 % predicted.

RESULTS

Both %LAV and Pi10 independently predicted either forced expiratory volume in 1 s/forced vital capacity (FEV1 /FVC) or FEV1 % predicted (P ≤ 0.001 for all standardized coefficients). However, the absolute values of the standardized coefficients were 2-3 times higher for %LAV than for Pi10 in all prediction models. The results were consistent in the two COPD cohorts.

CONCLUSIONS

%LAV predicts both FEV1 /FVC and FEV1 better than Pi10 in patients with COPD. Thus, emphysema may make a greater contribution to airflow limitation than airway remodelling in COPD.

Review of automatic pulmonary lobe segmentation methods from CT

The computational detection of pulmonary lobes from CT images is a challenging segmentation problem with important respiratory health care applications, including surgical planning and regional image analysis. Several authors have proposed automated algorithms and we present a methodological review. These algorithms share a number of common stages and we consider each stage in turn, comparing the methods applied by each author and discussing their relative strengths. No standard method has yet emerged and none of the published methods have been demonstrated across a full range of clinical pathologies and imaging protocols. We discuss how improved methods could be developed by combining different approaches, and we use this to propose a workflow for the development of new algorithms.

Automated anatomical labeling of abdominal arteries and hepatic portal system extracted from abdominal CT volumes

This paper proposes a method for automated anatomical labeling of abdominal arteries and a hepatic portal system. In abdominal surgeries, understanding blood vessel structure is critical since it is very complicated. The input of the proposed method is the blood vessel region extracted from the CT volume. The blood vessel region is expressed as a tree structure by applying a thinning process to it and compute the mapping from the branches in the tree structure to the anatomical names. First, several characteristic anatomical names are assigned by rule-based pre-processing. The branches assigned to these names are used as references. The remaining blood vessel names are assigned using a likelihood function trained by a machine-learning technique. Simple rule-based postprocessing can correct several blood vessel names. The output of the proposed method is a tree structure with anatomical names. In an experiment using 50 blood vessel regions manually extracted from abdominal CT volumes, the recall and precision rates of the abdominal arteries were 86.2% and 85.3%, and they were 86.5% and 79.5% for the hepatic portal system.

Topology preserving thinning of cell complexes

A topology preserving skeleton is a synthetic representation of an object that retains its topology and many of its significant morphological properties. The process of obtaining the skeleton, referred to as skeletonization or thinning, is a very active research area. It plays a central role in reducing the amount of information to be processed during image analysis and visualization, computer-aided diagnosis, or by pattern recognition algorithms. This paper introduces a novel topology preserving thinning algorithm, which removes simple cells-a generalization of simple points-of a given cell complex. The test for simple cells is based on acyclicity tables automatically produced in advance with homology computations. Using acyclicity tables render the implementation of thinning algorithms straightforward. Moreover, the fact that tables are automatically filled for all possible configurations allows to rigorously prove the generality of the algorithm and to obtain fool-proof implementations. The novel approach enables, for the first time, according to our knowledge, to thin a general unstructured simplicial complex. Acyclicity tables for cubical and simplicial complexes and an open source implementation of the thinning algorithm are provided as an additional material to allow their immediate use in the vast number of applications arising in medical imaging and beyond.

Regional ventilation changes in severe asthma after bronchial thermoplasty with (3)He MR imaging and CT

PURPOSE

To quantify regional lung ventilation in healthy volunteers and patients with severe asthma (both before and after thermoplasty) by using a combination of helium 3 ((3)He) magnetic resonance (MR) imaging and computed tomography (CT), with the intention of developing more effective image-guided treatments for obstructive lung diseases.

MATERIALS AND METHODS

With approval of the local institutional review board, informed consent, and an Investigational New Drug Exemption, six healthy volunteers and 10 patients with severe asthma were imaged in compliance with HIPAA regulations by using both multidetector CT and (3)He MR imaging. Individual bronchopulmonary segments were labeled voxel by voxel from the CT images and then registered to the (3)He MR images by using custom software. The (3)He signal intensity was then analyzed by evaluating the volume-weighted fraction of total-lung signal intensity present in each segment (segmental ventilation percentage [ SVP segmental ventilation percentage ]) and by identifying the whole-lung defect percentage and the segmental defect percentage. Of the 10 patients with asthma, seven received treatment with bronchial thermoplasty and were imaged with (3)He MR a second time. Changes in segmental defect percentages and whole-lung defect percentages are presented.

RESULTS

Ventilation measures for healthy volunteers yielded smaller segment-to-segment variation (mean SVP segmental ventilation percentage , 100% ± 18 [standard deviation]) than did the measures for patients with severe asthma (mean SVP segmental ventilation percentage , 97% ± 23). Patients with asthma also demonstrated larger segmental defect percentages (median, 13.5%; interquartile range, 8.9%-17.8%) than healthy volunteers (median, 6%; interquartile range, 5.6%-6.3%). These quantitative results confirm what is visually observed on the (3)He images. A Spearman correlation of r = -0.82 was found between the change in whole-lung defect percentage and the number of days between final treatment and second (3)He imaging.

CONCLUSION

Regional quantification of lung ventilation is indeed feasible and may be a useful technique for image-guided treatment of obstructive lung diseases, such as bronchial thermoplasty for severe asthma. In these patients, ventilation defects decreased as a function of time after treatment.

Longitudinal changes in airway remodeling and air trapping in severe asthma

RATIONALE AND OBJECTIVES

Previous cross-sectional studies have demonstrated that airway wall thickness and air trapping are greater in subjects with severe asthma than in those with mild-to-moderate asthma. However, a better understanding of how airway remodeling and lung density change over time is needed. This study aimed to evaluate predictors of airway wall remodeling and change in lung function and lung density over time in severe asthma.

MATERIALS AND METHODS

Phenotypic characterization and quantitative multidetector-row computed tomography (MDCT) of the chest were performed at baseline and ∼2.6 years later in 38 participants with asthma (severe n = 24 and mild-to-moderate n = 14) and nine normal controls from the Severe Asthma Research Program.

RESULTS

Subjects with severe asthma had a significant decline in postbronchodilator forced expiratory volume in 1 second percent (FEV1%) predicted over time (P < .001). Airway wall thickness measured by MDCT was increased at multiple airway generations in severe asthma compared to mild-to-moderate asthma (wall area percent [WA%]: P < .05) and normals (P < .05) at baseline and year 2. Over time, there was an increase in WA% and wall thickness percent (WT%) in all subjects (P = .030 and .009, respectively) with no change in emphysema-like lung or air trapping. Baseline prebronchodilator FEV1% inversely correlated with WA% and WT% (both P < .05). In a multivariable regression model, baseline WA%, race, and health care utilization were predictors of subsequent airway remodeling.

CONCLUSIONS

Severe asthma subjects have a greater decline in lung function over time than normal subjects or those with mild-to-moderate asthma. MDCT provides a noninvasive measure of airway wall thickness that may predict subsequent airway remodeling.

Airway wall area derived from 3-dimensional computed tomography analysis differs among lung lobes in male smokers

Background

It is time-consuming to obtain the square root of airway wall area of the hypothetical airway with an internal perimeter of 10 mm (√Aaw at Pi10), a comparable index of airway dimensions in chronic obstructive pulmonary disease (COPD), from all airways of the whole lungs using 3-dimensional computed tomography (CT) analysis. We hypothesized that √Aaw at Pi10 differs among the five lung lobes and √Aaw at Pi10 derived from one certain lung lobe has a high level of agreement with that derived from the whole lungs in smokers.

Methods

Pulmonary function tests and chest volumetric CTs were performed in 157 male smokers (102 COPD, 55 non-COPD). All visible bronchial segments from the 3^rd to 5^th generations were segmented and measured using commercially available 3-dimensional CT analysis software. √Aaw at Pi10 of each lung lobe was estimated from all measurable bronchial segments of that lobe.

Results

Using a mixed-effects model, √Aaw at Pi10 differed significantly among the five lung lobes (R² = 0.78, P<0.0001). The Bland-Altman plots show that √Aaw at Pi10 derived from the right or left upper lobe had a high level of agreement with that derived from the whole lungs, while √Aaw at Pi10 derived from the right or left lower lobe did not.

Conclusion

In male smokers, CT-derived airway wall area differs among the five lung lobes, and airway wall area derived from the right or left upper lobe is representative of the whole lungs.

AIRWAY LABELING USING A HIDDEN MARKOV TREE MODEL

We present a novel airway labeling algorithm based on a Hidden Markov Tree Model (HMTM). We obtain a collection of discrete points along the segmented airway tree using particles sampling [1] and establish topology using Kruskal's minimum spanning tree algorithm. Following this, our HMTM algorithm probabilistically assigns labels to each point. While alternative methods label airway branches out to the segmental level, we describe a general method and demonstrate its performance out to the subsubsegmental level (two generations further than previously published approaches). We present results on a collection of 25 computed tomography (CT) datasets taken from a Chronic Obstructive Pulmonary Disease (COPD) study.

Automatic anatomical labeling of the complete cerebral vasculature in mouse models

Study of cerebral vascular structure broadens our understanding of underlying variations, such as pathologies that can lead to cerebrovascular disorders. The development of high resolution 3D imaging modalities has provided us with the raw material to study the blood vessels in small animals such as mice. However, the high complexity and 3D nature of the cerebral vasculature make comparison and analysis of the vessels difficult, time-consuming and laborious. Here we present a framework for automated segmentation and recognition of the cerebral vessels in high resolution 3D images that addresses this need. The vasculature is segmented by following vessel center lines starting from automatically generated seeds and the vascular structure is represented as a graph. Each vessel segment is represented as an edge in the graph and has local features such as length, diameter, and direction, and relational features representing the connectivity of the vessel segments. Using these features, each edge in the graph is automatically labeled with its anatomical name using a stochastic relaxation algorithm. We have validated our method on micro-CT images of C57Bl/6J mice. A leave-one-out test performed on the labeled data set demonstrated the recognition rate for all vessels including major named vessels and their minor branches to be >75%. This automatic segmentation and recognition methods facilitate the comparison of blood vessels in large populations of subjects and allow us to study cerebrovascular variations.

Anatomical labeling of the Circle of Willis using maximum a posteriori probability estimation

Anatomical labeling of the cerebral arteries forming the Circle of Willis (CoW) enables inter-subject comparison, which is required for geometric characterization and discovering risk factors associated with cerebrovascular pathologies. We present a method for automated anatomical labeling of the CoW by detecting its main bifurcations. The CoW is modeled as rooted attributed relational graph, with bifurcations as its vertices, whose attributes are characterized as points on a Riemannian manifold. The method is first trained on a set of pre-labeled examples, where it learns the variability of local bifurcation features as well as the variability in the topology. Then, the labeling of the target vasculature is obtained as maximum a posteriori probability (MAP) estimate where the likelihood of labeling individual bifurcations is regularized by the prior structural knowledge of the graph they span. The method was evaluated by cross-validation on 50 subjects, imaged with magnetic resonance angiography, and showed a mean detection accuracy of 95%. In addition, besides providing the MAP, the method can rank the labelings. The proposed method naturally handles anatomical structural variability and is demonstrated to be suitable for labeling arterial segments of the CoW.

Genetic ancestry and the relationship of cigarette smoking to lung function and per cent emphysema in four race/ethnic groups: a cross-sectional study

BACKGROUND

Cigarette smoking is the major cause of chronic obstructive pulmonary disease and emphysema. Recent studies suggest that susceptibility to cigarette smoke may vary by race/ethnicity; however, they were generally small and relied on self-reported race/ethnicity.

OBJECTIVE

To test the hypothesis that relationships of smoking to lung function and per cent emphysema differ by genetic ancestry and self-reported race/ethnicity among Caucasians, African-Americans, Hispanics and Chinese-Americans.

DESIGN

Cross-sectional population-based study of adults age 45-84 years in the USA.

MEASUREMENTS

Principal components of genetic ancestry and continental ancestry estimated from one million genome-wide single nucleotide polymorphisms; pack-years of smoking; spirometry measured for 3344 participants; and per cent emphysema on computed tomography for 8224 participants.

RESULTS

The prevalence of ever-smoking was: Caucasians, 57.6%; African-Americans, 56.4%; Hispanics, 46.7%; and Chinese-Americans, 26.8%. Every 10 pack-years was associated with -0.73% (95% CI -0.90% to -0.56%) decrement in the forced expiratory volume in 1 s to forced vital capacity (FEV1 to FVC) and a 0.23% (95% CI 0.08% to 0.38%) increase in per cent emphysema. There was no evidence that relationships of pack-years to the FEV1 to FVC, airflow obstruction and per cent emphysema varied by genetic ancestry (all p>0.10), self-reported race/ethnicity (all p>0.10) or, among African-Americans, African ancestry. There were small differences in relationships of pack-years to the FEV1 among male Chinese-Americans and to the FEV1 to FVC ratio with African and Native American ancestry among male Hispanics only.

CONCLUSIONS

In this large cohort, there was little to no evidence that the associations of smoking to lung function and per cent emphysema differed by genetic ancestry or self-reported race/ethnicity.

Airway distensibility by HRCT in asthmatics and COPD with comparable airway obstruction

INTRODUCTION

Decreased airway distensibility (AD) in response to deep inspirations, as assessed by HRCT, has been associated with the severity of asthma and COPD.

AIMS

The current study was designed to compare the magnitude of AD by HRCT in individuals with asthma and COPD with comparable degrees of bronchial obstruction, and to explore factors that may influence it.

RESULTS

We enrolled a total of 12 asthmatics (M/F:7/5) and 8 COPD (7/1) with comparable degree of bronchial obstruction (FEV1% predicted mean ± SEM: 69.1 ± 5.2% and 61.2 ± 5.0%, respectively; p = 0.31). Each subject underwent chest HRCT at FRC and at TLC. A total of 701 airways (range 20 to 38 airway per subject; 2.0 to 23.1 mm in diameter) were analyzed. AD did not differ between asthmatics and COPD (mean ± SEM: 14 ± 3.5% and 17 ± 4.3%, respectively; p = 0.58). In asthmatics, AD was significantly associated with FEV1% predicted (r(2) = 0.45, p = 0.018). We found a significant correlation between the change in lung volume and the change in AD by HRCT (r(2) = 0.64, p = 0.002). In COPD, we found significant correlations between AD and the RV% predicted (r(2) = 0.51, p = 0.046) and the RV/TLC (r(2) = 0.68, p = 0.01).

CONCLUSIONS

AD was primarily affected by the dynamic ability to change lung volumes in asthmatics, and by static lung volumes in COPD.

Technical developments in postprocessing of paediatric airway imaging

CT postprocessing allows more scan information to be viewed at one time allowing an accurate diagnosis to be made more efficiently, and is particularly important in paediatric practice where invasive clinical diagnostic tools can be replaced or at least assisted by modern postprocessing techniques. Four visualization techniques in clinical use are described in this paper including the advantages and disadvantages of each: multiplanar reformation, maximum and minimum intensity projections, shaded surface display and volume rendering. Volume-rendered internal visualization in the form of virtual endoscopy is also discussed. In addition, the clinical usefulness in paediatric practice of demonstrating airway compression and its causes are discussed. Advanced postprocessing techniques that must still find their way from the biomedical research environment into clinical use are introduced with specific reference to computer-aided diagnosis.

Forced expiratory capnography and chronic obstructive pulmonary disease (COPD)

This report proposes a potentially sensitive and simple physiological method to detect early changes and to follow disease progression in obstructive pulmonary disease (COPD) based upon the usual pulmonary function test. Pulmonary function testing is a simple, although relatively insensitive, method to detect and follow COPD. As a proof-of-concept, we have examined the slope of the plateau for carbon dioxide during forced expiratory capnography in healthy (n = 10) and COPD subjects (n = 10). We compared the change in the rate of exhalation of carbon dioxide over time as a marker of heterogeneous ventilation of the lung. All subjects underwent pulmonary function testing, body-plethysmography, and forced exhalation capnography. The subjects with COPD also underwent high-resolution computed tomography of the chest. Regression lines were fitted to the slopes of the forced exhalation capnogram curves. There was no difference in the mean levels of exhaled carbon dioxide between the COPD and the healthy groups (p > 0.48). We found a significant difference in the mean slope of the forced exhalation capnogram for the COPD subjects compared to the healthy subjects (p = 0.01). Most important, for the COPD subjects, there was a significant positive correlation between the slope of the forced exhaled capnogram and a defined radiodensity measurement of the lung by high-resolution computed tomography (r(2) = 0.49, p = 0.02). The slope of the forced exhalation capnogram may be a simple way to determine physiological changes in the lungs in patients with COPD that are not obtainable with standard pulmonary function tests. Forced exhalation capnography would be of great clinical benefit if it can identify early disease changes and at-risk individuals.

Anatomical labeling of the circle of willis using maximum a posteriori graph matching

A new method for anatomically labeling the vasculature is presented and applied to the Circle of Willis. Our method converts the segmented vasculature into a graph that is matched with an annotated graph atlas in a maximum a posteriori (MAP) way. The MAP matching is formulated as a quadratic binary programming problem which can be solved efficiently. Unlike previous methods, our approach can handle non tree-like vasculature and large topological differences. The method is evaluated in a leave-one-out test on MRA of 30 subjects where it achieves a sensitivity of 93% and a specificity of 85% with an average error of 1.5 mm on matching bifurcations in the vascular graph.

Automated lobe-based airway labeling

Regional quantitative analysis of airway morphological abnormalities is of great interest in lung disease investigation. Considering that pulmonary lobes are relatively independent functional unit, we develop and test a novel and efficient computerized scheme in this study to automatically and robustly classify the airways into different categories in terms of pulmonary lobe. Given an airway tree, which could be obtained using any available airway segmentation scheme, the developed approach consists of four basic steps: (1) airway skeletonization or centerline extraction, (2) individual airway branch identification, (3) initial rule-based airway classification/labeling, and (4) self-correction of labeling errors. In order to assess the performance of this approach, we applied it to a dataset consisting of 300 chest CT examinations in a batch manner and asked an image analyst to subjectively examine the labeled results. Our preliminary experiment showed that the labeling accuracy for the right upper lobe, the right middle lobe, the right lower lobe, the left upper lobe, and the left lower lobe is 100%, 99.3%, 99.3%, 100%, and 100%, respectively. Among these, only two cases are incorrectly labeled due to the failures in airway detection. It takes around 2 minutes to label an airway tree using this algorithm.

Recent findings in chronic obstructive pulmonary disease by using quantitative computed tomography

Chronic obstructive pulmonary disease (COPD) is characterized by an incompletely reversible airflow limitation that results from a combination of airway wall remodeling and emphysematous lung destruction. Forced expiratory volume in 1s (FEV(1)) has been considered the gold standard for diagnosis, classification, and follow-up in patients with COPD, but it has certain limitations and it is still necessary to find other noninvasive modalities to complement FEV(1) to evaluate the effect of therapeutic interventions and the pathogenesis of COPD. Quantitative computed tomography (CT) has partly met this demand. The extent of emphysema and airway dimensions measured using quantitative CT are associated with morphological and functional changes and clinical symptoms in patients with COPD. Phenotyping COPD based on quantitative CT has facilitated interventional and genotypic studies. Recent advances in COPD findings with quantitative CT are discussed in this review.

CT based computerized identification and analysis of human airways: a review

As one of the most prevalent chronic disorders, airway disease is a major cause of morbidity and mortality worldwide. In order to understand its underlying mechanisms and to enable assessment of therapeutic efficacy of a variety of possible interventions, noninvasive investigation of the airways in a large number of subjects is of great research interest. Due to its high resolution in temporal and spatial domains, computed tomography (CT) has been widely used in clinical practices for studying the normal and abnormal manifestations of lung diseases, albeit there is a need to clearly demonstrate the benefits in light of the cost and radiation dose associated with CT examinations performed for the purpose of airway analysis. Whereas a single CT examination consists of a large number of images, manually identifying airway morphological characteristics and computing features to enable thorough investigations of airway and other lung diseases is very time-consuming and susceptible to errors. Hence, automated and semiautomated computerized analysis of human airways is becoming an important research area in medical imaging. A number of computerized techniques have been developed to date for the analysis of lung airways. In this review, we present a summary of the primary methods developed for computerized analysis of human airways, including airway segmentation, airway labeling, and airway morphometry, as well as a number of computer-aided clinical applications, such as virtual bronchoscopy. Both successes and underlying limitations of these approaches are discussed, while highlighting areas that may require additional work.