A differential geometric approach to automated segmentation of human airway tree

Predicting Postoperative Lung Cancer Recurrence and Survival Using Cox Proportional Hazards Regression and Machine Learning

BACKGROUND

Surgical resection remains the standard treatment for early-stage lung cancer. However, the recurrence rate after surgery is unacceptably high, ranging from 30% to 50%. Despite extensive efforts, accurately predicting the likelihood and timing of recurrence remains a significant challenge. This study aims to predict postoperative recurrence by identifying novel image biomarkers from preoperative chest CT scans.

METHODS

A cohort of 309 patients was selected from 512 non-small-cell lung cancer patients who underwent lung resection. Cox proportional hazards regression analysis was employed to identify risk factors associated with recurrence and was compared with machine learning (ML) methods for predictive performance. The goal is to improve the ability to predict the risk and time of recurrence in seemingly "cured" patients, enabling personalized surveillance strategies to minimize lung cancer recurrence.

RESULTS

The Cox hazards analyses identified surgical procedure, TNM staging, lymph node involvement, body composition, and tumor characteristics as significant determinants of recurrence risk, both for local/regional and distant recurrence, as well as recurrence-free survival (RFS) and overall survival (OS) (p < 0.05). ML models and Cox models exhibited comparable predictive performance, with an area under the receiver operative characteristic (ROC) curve (AUC) ranging from 0.75 to 0.77.

CONCLUSIONS

These promising findings demonstrate the feasibility of predicting postoperative lung cancer recurrence and survival time using preoperative chest CT scans. However, further validation using larger, multisite cohort is necessary to ensure robustness and facilitate integration into clinical practice for improved cancer management.

CT-Derived Features as Predictors of Clot Burden and Resolution

Objectives: To evaluate the prognostic utility of CT-imaging-derived biomarkers in distinguishing acute pulmonary embolism (PE) resolution and its progression to chronic PE, as well as their association with clot burden. Materials and Methods: We utilized a cohort of 45 patients (19 male (42.2%)) and 96 corresponding CT scans with exertional dyspnea following an acute PE. These patients were referred for invasive cardiopulmonary exercise testing (CPET) at the University of Pittsburgh Medical Center from 2018 to 2022, for whom we have ground truth classification of chronic PE, as well as CT-derived features related to body composition, cardiopulmonary vasculature, and PE clot burden using artificial intelligence (AI) algorithms. We applied Lasso regularization to select parameters, followed by (1) Ordinary Least Squares (OLS) regressions to analyze the relationship between clot burden and the selected parameters and (2) logistic regressions to differentiate between chronic and resolved patients. Results: Several body composition and cardiopulmonary factors showed statistically significant association with clot burden. A multivariate model based on cardiopulmonary features demonstrated superior performance in predicting PE resolution (AUC: 0.83, 95% CI: 0.71-0.95), indicating significant associations between airway ratio (negative correlation), aorta diameter, and heart volume (positive correlation) with PE resolution. Other multivariate models integrating demographic features showed comparable performance, while models solely based on body composition and baseline clot burden demonstrated inferior performance. Conclusions: Our analysis suggests that cardiopulmonary and demographic features hold prognostic value for predicting PE resolution, whereas body composition and baseline clot burden do not. Clinical Relevance: Our identified prognostic factors may facilitate the follow-up procedures for patients diagnosed with acute PE.

Determination of pulmonary vessel alteration in Chinese male smokers by quantitative computed tomography measurements: a retrospective study

Background

The blood volume of intraparenchymal vessels is reported to be increased in smokers. However, the blood volume can be affected by many confounders besides tobacco exposure. This study aimed to investigate the association between cigarette smoking and pulmonary blood volume after adjusting the related factors in a large cohort of Chinese males.

Methods

In this retrospective study, male participants admitted to the First Affiliated Hospital of Xi'an Jiaotong University for annual health assessment between February 2017 and February 2018 were enrolled. All subjects underwent non-contrast chest computed tomography (CT) scans, and 152 subjects underwent a review CT scan 2-3 years later. A three-dimensional approach was employed to segment the lung and intrapulmonary vessels and quantitative CT (QCT) measurements, including lung volume (LV), intrapulmonary vessel volume (IPVV), low-attenuation area <-950 Hounsfield unit (LAA-950 and LAA-950%), and mean lung density (MLD). Linear regression was used to estimate the association between IPVV and the smoking index (SI). A paired t-test was used to compare the QCT parameters between the initial and follow-up CT scans.

Results

A total of 656 male participants were enrolled and classified into three subgroups: non-smokers (n=311), current smokers (n=267), and former smokers (n=78). The IPVV of current smokers (134.62±23.96 vs. 120.76±25.52 mL) and former smokers (130.79±25.13 vs. 120.76±25.52 mL) were significantly larger than that of non-smokers (P<0.05). A higher SI was associated with greater IPVV [non-standardized coefficient: 0.167, 95% confidence interval (CI): 0.086-0.248]. For current smokers, the IPVV of the follow-up scan significantly increased compared to its baseline scan (135.49±28.60 vs. 129.73±29.75 mL, t=-2.326, P=0.02), but for the non-smokers and former smokers, the IPVV of the follow-up scan did not increase or decrease compared to the baseline scan (P>0.05).

Conclusions

Pulmonary vascular volumes detectable on non-contrast CT are associated with cigarette exposure, and smoking cessation may prevent pulmonary vasculature remodeling.

Deep learning-based bronchial tree-guided semi-automatic segmentation of pulmonary segments in computed tomography images

Background

Pulmonary segments are valuable because they can provide more precise localization and intricate details of lung cancer than lung lobes. With advances in precision therapy, there is an increasing demand for the identification and visualization of pulmonary segments in computed tomography (CT) images to aid in the precise treatment of lung cancer. This study aimed to integrate multiple deep-learning models to accurately segment pulmonary segments in CT images using a bronchial tree (BT)-based approach.

Methods

The proposed segmentation method for pulmonary segments using the BT-based approach comprised the following five essential steps: (I) segmentation of the lung using a U-Net (R231) (public access) model; (II) segmentation of the lobes using a V-Net (self-developed) model; (III) segmentation of the airway using a combination of a differential geometric approach method and a BronchiNet (public access) model; (IV) labeling of the BT branches based on anatomical position; and (V) segmentation of the pulmonary segments based on the distance of each voxel to the labeled BT branches. This five-step process was applied to 14 high-resolution breath-hold CT images and compared against manual segmentations for evaluation.

Results

For the lung segmentation, the lung mask had a mean dice similarity coefficient (DSC) of 0.98±0.03. For the lobe segmentation, the V-Net model had a mean DSC of 0.94±0.06. For the airway segmentation, the average total length of the segmented airway trees per image scan was 1,902.8±502.1 mm, and the average number of the maximum airway tree generations was 8.5±1.3. For the segmentation of the pulmonary segments, the proposed method had a DSC of 0.73±0.11 and a mean surface distance of 6.1±2.9 mm.

Conclusions

This study demonstrated the feasibility of combining multiple deep-learning models for the auxiliary segmentation of pulmonary segments on CT images using a BT-based approach. The results highlighted the potential of the BT-based method for the semi-automatic segmentation of the pulmonary segment.

Human treelike tubular structure segmentation: A comprehensive review and future perspectives

Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed.

Sex-related Differences in Airway Dimensions: A Study Based on Quantitative Computed Tomography among Chinese Population

Sex-dependent radiation injury may be related to the differences in physiological characteristics between the sexes. This study aimed to better understand variations in airway dimensions among male and female Chinese non-smokers. This study included 970 adults and 45 children who underwent chest CT. All participants were non-smokers, without current or former chronic pulmonary disease, and all underwent CT examination. The CT images were quantitatively assessed, providing airway dimensions. The differences in inner diameter, wall thickness, wall area (WA), and WA% for each airway were compared between male and female patients. Sex is an important influencing factor in airway morphological parameters. These parameters are different between men and women: men have a larger airway diameter (P < 0.05) and smaller wall area (WA%, P < 0.05) compared with women. Younger women (<35 years) have a greater diameter and smaller WA% compared with older women (P < 0.05). Sex-related differences in airway morphology were not observed in pediatric participants. Significant differences were found in quantitative CT measures of WA% and an internal diameter among non-smokers of varying sex. The differences found in this study might explain, in part, sex-dependency of radiation injury and a possible radiological protection scheme.

Application of Deep Convolution Network to Automated Image Segmentation of Chest CT for Patients With Tumor

Objectives

To automate image delineation of tissues and organs in oncological radiotherapy by combining the deep learning methods of fully convolutional network (FCN) and atrous convolution (AC).

Methods

A total of 120 sets of chest CT images of patients were selected, on which radiologists had outlined the structures of normal organs. Of these 120 sets of images, 70 sets (8,512 axial slice images) were used as the training set, 30 sets (5,525 axial slice images) as the validation set, and 20 sets (3,602 axial slice images) as the test set. We selected 5 published FCN models and 1 published Unet model, and then combined FCN with AC algorithms to generate 3 improved deep convolutional networks, namely, dilation fully convolutional networks (D-FCN). The images in the training set were used to fine-tune and train the above 8 networks, respectively. The images in the validation set were used to validate the 8 networks in terms of the automated identification and delineation of organs, in order to obtain the optimal segmentation model of each network. Finally, the images of the test set were used to test the optimal segmentation models, and thus we evaluated the capability of each model of image segmentation by comparing their Dice coefficients between automated and physician delineation.

Results

After being fully tuned and trained with the images in the training set, all the networks in this study performed well in automated image segmentation. Among them, the improved D-FCN 4s network model yielded the best performance in automated segmentation in the testing experiment, with an global Dice of 87.11%, and a Dice of 87.11%, 97.22%, 97.16%, 89.92%, and 70.51% for left lung, right lung, pericardium, trachea, and esophagus, respectively.

Conclusion

We proposed an improved D-FCN. Our results showed that this network model might effectively improve the accuracy of automated segmentation of the images in thoracic radiotherapy, and simultaneously perform automated segmentation of multiple targets.

Computer-aided quantitative MSCT measurements may be useful for congenital lung malformations surgical approach selection

PURPOSE

To examine the association between the MSCT quantitative measurements of congenital lung malformations (CLM) and the selection of surgical approaches (lobectomy vs. lung-sparing surgery).

METHODS

This retrospective study evaluated CLM surgical cases at our institution from 2016 to 2018. MSCT quantitative measurements were generated by a semi-automated approach: the volume of the lesion (V_lesion), the volume of the lesion-involved lobe (V_lobe), the volume of the lesion-involved lung (V_lung) and the volume of the total lung (V_{total lung}). The proportions of V_lesion to V_lobe (P_lesion/lobe), V_lesion to V_lung (P_lesion/lung), and V_lesion to V _{total lung} (P_{lesion/total lung}) were calculated. We used Logistics regression to examine whether quantitative measurements were associated with the selection of surgical approaches.

RESULTS

151 patients were included (median age at surgery 6 months). 82 patients underwent lung-sparing surgery, and 69 patients underwent lobectomy. V_lesion (OR 1.51, 95% CI 1.09-2.07), P_lesion/lobe (OR 1.78, 95% CI 1.16-2.72), P_lesion/lung (OR 1.63, 95% CI 1.13-2.35), and P_{lesion/total lung} (OR 1.58, 95% CI 1.12-2.22) were positively associated with the selection of lobectomy.

CONCLUSION

The application of quantified MSCT analysis may provide insight into the quantitative characteristics of CLM, which could be potentially useful for surgical approach selection.

Automated quantification of COVID-19 severity and progression using chest CT images

OBJECTIVE

To develop and test computer software to detect, quantify, and monitor progression of pneumonia associated with COVID-19 using chest CT scans.

METHODS

One hundred twenty chest CT scans from subjects with lung infiltrates were used for training deep learning algorithms to segment lung regions and vessels. Seventy-two serial scans from 24 COVID-19 subjects were used to develop and test algorithms to detect and quantify the presence and progression of infiltrates associated with COVID-19. The algorithm included (1) automated lung boundary and vessel segmentation, (2) registration of the lung boundary between serial scans, (3) computerized identification of the pneumonitis regions, and (4) assessment of disease progression. Agreement between radiologist manually delineated regions and computer-detected regions was assessed using the Dice coefficient. Serial scans were registered and used to generate a heatmap visualizing the change between scans. Two radiologists, using a five-point Likert scale, subjectively rated heatmap accuracy in representing progression.

RESULTS

There was strong agreement between computer detection and the manual delineation of pneumonic regions with a Dice coefficient of 81% (CI 76-86%). In detecting large pneumonia regions (> 200 mm3), the algorithm had a sensitivity of 95% (CI 94-97%) and specificity of 84% (CI 81-86%). Radiologists rated 95% (CI 72 to 99) of heatmaps at least "acceptable" for representing disease progression.

CONCLUSION

The preliminary results suggested the feasibility of using computer software to detect and quantify pneumonic regions associated with COVID-19 and to generate heatmaps that can be used to visualize and assess progression.

KEY POINTS

• Both computer vision and deep learning technology were used to develop computer software to quantify the presence and progression of pneumonia associated with COVID-19 depicted on CT images. • The computer software was tested using both quantitative experiments and subjective assessment. • The computer software has the potential to assist in the detection of the pneumonic regions, monitor disease progression, and assess treatment efficacy related to COVID-19.

Lung involvement in patients with coronavirus disease-19 (COVID-19): a retrospective study based on quantitative CT findings

Purpose

To explore lung involvement in patients with coronavirus disease-19 (COVID-19) using quantitative computed tomography (QCT).

Methods

A total of 52 patients with COVID-19 who were admitted to three hospitals in China from January 23, 2020 to February 1, 2020 were retrospectively analyzed using QCT. The accuracy of QCT segmentation was assessed. The relationship between the time from symptom onset to initial CT and QCT parameters acquired on the initial CT were explored.

Results

First, the ability of QCT to detect and segment lesions was investigated and it was unveiled that results of segmentation of the majority of cases (42/52) were satisfactory and for 8 out of 52 patients, the images depicted lesions with miss-segmentation; besides, 2 out of 52 cases had negative finding on chest CT achieved by both radiologists and QCT. QCT-related parameters showed to have a relationship with the time from symptom onset to initial CT. In the early-stage (0-3 days), the percentage of lung involvement was 4%, with a mean density of - 462 ± 99 HU. The peak density of lesions appeared at the range of - 500 to - 700 HU on density histogram. In the intermediate-stage (4-6 days), the mean percentage of lung involvement noticeably increased compared with that in early stage (7%, p < 0.05). In late stage (7-14 days), the percentage of lung involvement decreased to 5%. The mean density of lesions was the highest (- 430 ± 80), and heterogeneity density distribution showed a dual-peak on density histogram.

Conclusion

COVID-19 can be promptly detected by QCT. In addition, the QCT-related parameters can highly facilitate assessment of pulmonary involvement.

Risk stratification of acute pulmonary embolism based on the clot volume and right ventricular dysfunction on CT pulmonary angiography

OBJECTIVE

To test the feasibility of the clot volume and right ventricular dysfunction for risk stratification of acute pulmonary embolism (APE) patients.

METHODS

CT pulmonary angiography (CTPA) images of 158 APE patients were collected. After excluding 38 (24.1%) patients due to unsatisfactory quality, 120 APE patients (61 males and 59 females) were divided into high-risk (n = 37) and non-high-risk (n = 83) groups. Clot burden was measured by an automated programme (clot volume) and by two semi-quantitative systems (Qanadli and Mastora scores). The ratios of the right ventricular diameter to left ventricular diameter (RVd/LVd) and area (RVa/LVa) were obtained. The correlations amongst the above parameters were analysed. Receiver operating characteristic (ROC) curves were calculated to determine the efficacy of high-risk APE. Multivariate analyses were used to identify the independent predictors.

RESULTS

Strong positive correlations were found between the clot volume and both Qanadli score (r²  = 0.696, P < 0.001) and Mastora score (r²  = 0.728, P < 0.001), and moderate correlations were found between the clot volume and both RVd/LVd (r²  = 0.392, P < 0.001) and RVa/LVa (r²  = 0.389, P < 0.001). The clot volume contributed the highest efficacy (AUC = 0.992) for the identification of high-risk cases, followed by Mastora score (0.968), Qanadli score (0.952), RVa/LVa (0.900) and RVd/LVd (0.892). The clot volume and RVd/LVd were two independent factors of high-risk APE.

CONCLUSIONS

The clot volume is correlated with semi-quantitative clot burden scores and CT measured cardiac parameters. The clot volume and RVd/LVd were two independent factors of high-risk APE patients.

Determination of vascular alteration in smokers by quantitative computed tomography measurements

A new method of quantitative computed tomography (CT) measurements of pulmonary vessels are applicable to morphological studies and may be helpful in defining the progression of emphysema in smokers. However, limited data are available on the relationship between the smoking status and pulmonary vessels alteration established in longitudinal observations. Therefore, we investigated the change of pulmonary vessels on CTs in a longitudinal cohort of smokers.Chest CTs were available for 287 current smokers, 439 non-smokers, and 80 former smokers who quit smoking at least 2 years after the baseline CT. CT images obtained at the baseline and 1 year later were assessed by a new quantitative CT measurement method, computing the total number of pulmonary vessels (TNV), mean lung density (MLD), and the percentage of low-attenuation areas at a threshold of -950 (density attenuation area [LAA]%950). Analysis of variance (ANOVA) and the independent sample t test were used to estimate the influence of the baseline parameters. The t paired test was employed to evaluate the change between the baseline and follow-up results.The current smokers related to have higher whole-lung MLD, as well as less and lower TNV values than the non-smokers (P <.05). But no significant differences in LAA%950 were found between smokers and non-smokers. After one year, the increase in LAA%950 was more rapid in the current (additional 0.3% per year, P <. 05-.01) than in the former smokers (additional 0.2% per year, P = .3). Additionally, the decline in TNV was faster in the current (additional -1.3 per year, P <.05-.01) than that in the former smokers (additional -0.2 per year, P = .6). Current smoke, pack-years, weight, and lung volume independently predicted TNV at baseline (P <.001) in multivariate analysis.The findings of this study reveal that the decline in the pulmonary vessels in smokers can be measured and related to their smoking status.

Morphological analysis of blood vessels near lung tumors using 3-D quantitative CT

BACKGROUND

Improved visualization of lung cancer-associated vessels is vital.

OBJECTIVE

To evaluate the efficacy of 3-D quantitative CT in lung cancer-associated pulmonary vessel assessment.

METHODS

Vascular CT changes were assessed visually and using FACT-Digital lung TM software (n = 162 patients, 178 controls). The total number of pulmonary vessels (TNV) and mean lumen area of pulmonary vessels (MAV) vertical to cross-sections of fifth/sixth-generation bronchioles were measured.

RESULTS

Visual investigation revealed fewer ipsilateral pulmonary vascular abnormalities in lung cancer (151/162) than did quantitative CT (162/162), and required more time (3.2±1.5 vs. 2.5±1.3 min) (P < 0.05). CT measurements revealed that the TNV vertical to the fifth-generation bronchial cross-section of the ipsilateral, contralateral, and control groups was 14.58±4.75, 9.58±3.74, and 10.22±4.07 and the MAV in these groups was 99.70±26.20, 58.76±29.29, and 57.76±18.32, respectively. The TNV vertical to the sixth-generation bronchial cross-section of the ipsilateral, contralateral, and control groups was 16.64±5.14, 11.59±4.06, and 11.75±4.16 and the MAV was 110.22±31.47, 67.62±30.41, and 60.24±16.18, respectively. The TNV and MAV in ipsilateral lung cancer tissues exceeded those in the contralateral side and control group tissues (P < 0.001).

CONCLUSIONS

Automated 3-D quantitative CT could successfully characterize pulmonary vessels and their lung cancer-associated changes.

Application of automated bronchial 3D-CT measurement in pulmonary contusion complicated with acute respiratory distress syndrome

BACKGROUNDQuantitative measurement of bronchial morphological changes in pulmonary contusion with acute respiratory distress syndrome (ARDS) has important clinical implications.OBJECTIVETo investigate the morphological changes in bronchus before and after treatment in patients with pulmonary contusion combined with ARDS using an automated bronchial three-dimensional computed tomography (3D-CT) measurement method.METHODSThe study involves a dataset of CT images of 62 patients diagnosed with pulmonary contusion combined with ARDS. The volume of pulmonary contusion lesions was calculated as a percentage of the total lung volume using the automated 3D-CT method. The bronchial luminal cross-sectional area, wall cross-sectional area, the maximum and average wall thickness, the maximum and average luminal densities, intraluminal and extraluminal diameters, and circumferences of generations 2-4 bronchi before and after treatment were measured. Furthermore, the corresponding differences were analyzed statistically.RESULTSThe luminal cross-sectional area, wall cross-sectional area, intraluminal and extraluminal diameters, and circumferences of generations 2-4 bronchi were all significantly lower before treatment than after treatment (P < 0.05). However, the maximum and average wall thicknesses were both significantly higher before treatment than after treatment (P < 0.05). No significant difference was found in the maximum and average luminal densities before and after treatment (P > 0.05). The percentage of the pulmonary contusion lesion volume to the total lung volume correlated positively with the thoracic trauma severity score (r = 0.74, P < 0.01).CONCLUSIONSQuantitative bronchial CT image analysis enables to detect and assess bronchial morphological changes in patients diagnosed with pulmonary contusion combined with ARDS.

Imaging Features of Chronic Bronchitis with Preserved Ratio and Impaired Spirometry (PRISm)

PURPOSE

The purpose of the study was to investigate the quantitative chest tomographic features of chronic bronchitis with preserved ratio and impaired spirometry (PRISm), including airway wall area, emphysema index, and lung capacity.

METHODS

An observational, cross-sectional study of 343 patients at the Ninth Hospital of Xi'an Affiliated Hospital of Xi'an Jiaotong University between October 2014 and September 2017. The patients were divided into three groups: 77 cases of chronic bronchitis with normal lung function (forced expiratory volume in one second/forced vital capacity) (FEV1/FVC > 70%, FEV1%pred > 80%), 80 cases of chronic bronchitis with PRISm (FEV1/FVC > 70%, FEV1%pred < 80%), and 186 cases of the early chronic obstructive pulmonary disease (COPD) (FEV1/FVC < 70%, FEV1%pred > 50%, that is, Global Initiative for Chronic Obstructive Lung Disease (GOLD) grade 1 + 2). We compared and analyzed the differences in imaging between the chronic bronchitis with PRISm and the other two groups.

RESULTS

Compared with the early COPD group, the PRISm group revealed significant differences in airway wall area, emphysema index, and lung capacity (P < 0.05). Compared with the chronic bronchitis with normal lung function group, the PRISm group showed increased WA%_LUL5, decreased lung capacity, and higher mean lung density.

CONCLUSION

In terms of airway wall area and emphysema index, patients with chronic bronchitis with PRISm were essentially no different than those with chronic bronchitis without abnormal spirometry, whereas for symptoms, they are more like GOLD 1 and 2 patients. Our findings show that it is not yet clear whether it constitutes an intermediate stage of chronic bronchitis with normal lung function that progression to early COPD.

The features of AECOPD with carbon dioxide retention

Background

Chronic obstructive pulmonary disease (COPD) with carbon dioxide retention is associated with a worsening clinical condition and the beginning of pulmonary ventilation decompensation. This study aimed to identify the factors associated with carbon dioxide retention.

Methods

This was a retrospective study of consecutive patients with COPD (meeting the Global Initiative for Chronic Obstructive Lung Disease diagnostic criteria) hospitalized at The Ninth Hospital of Xi’an Affiliated Hospital of Xi’an Jiaotong University between October 2014 and September 2017. The baseline demographic, clinical, laboratory, pulmonary function, and imaging data were compared between the 86 cases with carbon dioxide retention and the 144 cases without carbon dioxide retention.

Results

Compared with the non-carbon dioxide retention group, the group with carbon dioxide retention had a higher number of hospitalizations in the previous 12 months (p = 0.013), higher modified Medical Research Council (mMRC) dyspnea scores (p = 0.034), lower arterial oxygen pressure (p = 0.018), worse pulmonary function (forced expiratory volume in one second/forced vital capacity [FEV₁/FVC; p < 0.001], FEV₁%pred [p < 0.001], Z5%pred [p = 0.004], R5%pred [p = 0.008], R5-R20 [p = 0.009], X5 [p = 0.022], and Ax [p = 0.011]), more severe lung damage (such as increased lung volume [p = 0.011], more emphysema range [p = 0.007], and lower mean lung density [p = 0.043]). FEV₁ < 1 L (odds ratio [OR] = 4.011, 95% confidence interval [CI]: 2.216–7.262) and emphysema index (EI) > 20% (OR = 1.926, 95% CI: 1.080–3.432) were independently associated with carbon dioxide retention in COPD.

Conclusion

Compared with the non-carbon dioxide retention group, the group with carbon dioxide retention had different clinical, pulmonary function, and imaging features. FEV₁ < 1 L and EI > 20% were independently associated with carbon dioxide retention in AECOPD.

Trial registration

ChiCTR-OCH-14004904. Registered 25 June 2014.

Electronic supplementary material

The online version of this article (10.1186/s12890-018-0691-8) contains supplementary material, which is available to authorized users.

The pulmonary damage caused by smoking: A longitudinal study

BACKGROUND

Smoking for a long period is known to cause several harms to the human body, chiefly associated with serious pulmonary damage.

OBJECTIVE

The purpose of this study was to evaluate the difference in the pulmonary damage between current smokers and ex-smokers, through measuring the bronchial parameters and the extent of emphysema, in order to further illustrate the harm of smoking and the need to stop smoking.

METHODS

Using the FACT-Digital lung TM software quantitatively analysis of CT images, including the WT, WA%, LD, LV, PD, and %LAA-950 was performed. The percentage of low attenuation areas less than -950 Hounsfield units (%LAA-950) was defined as the extent of emphysema. The longitudinal data in the two consecutive years of these current smoker group and ex-smoker group were compared by paired t-test.

RESULTS

The LV, %LAA-950, WT and WA% of current smokers increased more rapidly each year than that of ex-smokers. The PD and LD of current smokers declined more rapidly each year than that of ex-smokers.

CONCLUSIONS

This study shows that pulmonary damage caused by smoking related to the smoking status, can be measured. Smoking cessation has a positive role in alleviating the progress of pulmonary damage.

Detection of Lung Contour with Closed Principal Curve and Machine Learning

Radiation therapy plays an essential role in the treatment of cancer. In radiation therapy, the ideal radiation doses are delivered to the observed tumor while not affecting neighboring normal tissues. In three-dimensional computed tomography (3D-CT) scans, the contours of tumors and organs-at-risk (OARs) are often manually delineated by radiologists. The task is complicated and time-consuming, and the manually delineated results will be variable from different radiologists. We propose a semi-supervised contour detection algorithm, which firstly uses a few points of region of interest (ROI) as an approximate initialization. Then the data sequences are achieved by the closed polygonal line (CPL) algorithm, where the data sequences consist of the ordered projection indexes and the corresponding initial points. Finally, the smooth lung contour can be obtained, when the data sequences are trained by the backpropagation neural network model (BNNM). We use the private clinical dataset and the public Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset to measure the accuracy of the presented method, respectively. To the private dataset, experimental results on the initial points which are as low as 15% of the manually delineated points show that the Dice coefficient reaches up to 0.95 and the global error is as low as 1.47 × 10^-2. The performance of the proposed algorithm is also better than the cubic spline interpolation (CSI) algorithm. While on the public LIDC-IDRI dataset, our method achieves superior segmentation performance with average Dice of 0.83.

Risk factors predict frequent hospitalization in patients with acute exacerbation of COPD

Purpose

COPD is a heterogeneous disease, and the available prognostic indexes are therefore limited. This study aimed to identify the factors associated with acute exacerbation leading to hospitalization.

Patients and methods

This was a retrospective study of consecutive patients with COPD (meeting the Global Initiative for Chronic Obstructive Lung Disease [GOLD] diagnostic criteria) hospitalized at the Ninth Hospital of Xi’an Affiliated Hospital of Xi’an Jiaotong University between October 2014 and September 2016. During follow-up after first hospitalization, the patients who had been rehospitalized within 1 year for acute exacerbation were grouped into the frequent exacerbation (FE) group, while the others were grouped into the infrequent exacerbation (IE) group. The baseline demographic, clinical, laboratory, pulmonary function, and imaging data were compared between the two groups.

Results

Compared with the IE group, the FE group had lower forced expiratory volume in 1 second (FEV₁)/forced vital capacity (FVC) (P=0.005), FEV_1%pred (P=0.002), maximal mid-expiratory flow (MMEF_25–75%pred) (P=0.003), and ratio of carbon monoxide diffusion capacity to alveolar ventilation (DLCO/VA) (P=0.03) and higher resonant frequency (F_res; P=0.04). According to generations of bronchi, the percentage of the wall area (%WA) of lobes was found to be higher in the FE group. Emphysema index (EI), mean emphysema density (MED)_whole and MED_{left lung} in the FE group were significantly worse than in the IE group (P<0.05). Using logistic regression, exacerbation hospitalizations in the past year (odds ratio [OR] 14.4, 95% CI 6.1–34.0, P<0.001) and EI >10% (OR 2.9, 95% CI 1.2–7.1, P=0.02) were independently associated with frequent acute exacerbation of COPD (AECOPD) hospitalization.

Conclusion

Exacerbation hospitalizations in the past year and imaging features of emphysema (EI) were independently associated with FE hospitalization.

Vasculature surrounding a nodule: A novel lung cancer biomarker

PURPOSE

To investigate whether the vessels surrounding a nodule depicted on non-contrast, low-dose computed tomography (LDCT) can discriminate benign and malignant screen detected nodules.

MATERIALS AND METHODS

We collected a dataset consisting of LDCT scans acquired on 100 subjects from the Pittsburgh Lung Screening study (PLuSS). Fifty subjects were diagnosed with lung cancer and 50 subjects had suspicious nodules later proven benign. For the lung cancer cases, the location of the malignant nodule in the LDCT scans was known; while for the benign cases, the largest nodule in the LDCT scan was used in the analysis. A computer algorithm was developed to identify surrounding vessels and quantify the number and volume of vessels that were connected or near the nodule. A nonparametric receiver operating characteristic (ROC) analysis was performed based on a single nodule per subject to assess the discriminability of the surrounding vessels to provide a lung cancer diagnosis. Odds ratio (OR) were computed to determine the probability of a nodule being lung cancer based on the vessel features.

RESULTS

The areas under the ROC curves (AUCs) for vessel count and vessel volume were 0.722 (95% CI=0.616-0.811, p<0.01) and 0.676 (95% CI=0.565-0.772), respectively. The number of vessels attached to a nodule was significantly higher in the lung cancer group 9.7 (±9.6) compared to the non-lung cancer group 4.0 (±4.3) CONCLUSION: Our preliminary results showed that malignant nodules are often surrounded by more vessels compared to benign nodules, suggesting that the surrounding vessel characteristics could serve as lung cancer biomarker for indeterminate nodules detected during LDCT lung cancer screening using only the information collected during the initial visit.

Sex-related differences in bronchial parameters and pulmonary function test results in patients with chronic obstructive pulmonary disease based on three-dimensional quantitative computed tomography

Objective

This study was performed to evaluate the effect of sex on bronchial parameters and the predicted forced expiratory volume in 1 s expressed as a percentage of the forced vital capacity (FEV1% pred) on pulmonary function testing.

Methods

The data of 359 patients with chronic obstructive pulmonary disease (COPD) with available FEV1% pred and computed tomography (CT) images were retrospectively reviewed. FACT-Digital lung TM software (DeXin, Xi’an, China) was used to perform fully automated three-dimensional CT quantitative measurements of the bronchi. Generation 5 to 7 bronchi were measured, and the parameters analyzed were the lumen diameter (LD), wall thickness (WT), lumen area (LA), and WA% [WA / (WA + LA) × 100%].

Results

In the smoking, smoking cessation, and nonsmoking groups, women had a significantly larger WA% and smaller LD, WT, and LA than men. The FEV1% pred was significantly lower in women than men in the smoking and smoking cessation groups. The FEV1% pred was significantly higher in women than men in the nonsmoking group.

Conclusion

Sex-related differences may partially explain why smoking women experience more severe pulmonary function impairment than men among patients with COPD.

A three-dimensional approach for identifying small pulmonary vessels in smokers

BACKGROUND

This study aims to develop a computerized scheme that utilizes a differential geometric approach to identify pulmonary vessels and then evaluate the performance of the scheme on the CT images of heavy smokers.

METHODS

The scheme consists of two primary steps to segment entire lung vascular tree and identify the number of pulmonary vessels in a cross section. The scheme performance including accuracy, consistency, and efficiency was assessed using 102 chest CT scans. Further assessment was performed on the relationship between pulmonary vessels and the extent of emphysema as well as pulmonary artery alteration.

RESULTS

The mean number of vessels in the cross section at the 5th generation was 17.84±4.74 and 17.23±4.85 assessed by computerized scheme and radiologists, respectively, which are significantly different (t = 2.12, p = 0.055). The results were consistent with those obtained by using a semi-automatic tool (r = 0.75, p = 0.01). In addition, in the 5th generation, the mean number of vessels was inversely related to the percentage of the low attenuation area (r = -0.704, p = 0.000), the mean lumen area of pulmonary vessel was inversely related to the mean value of main pulmonary artery diameter (r = -0.617, p = 0.000). The computational time of segmenting vessels was 6.50±0.02 seconds, which is much less than the average 8 minutes of the time spent by radiologists using the semi-automatic tool.

CONCLUSION

Applying the computerized scheme yields reasonable performance on the segmentation of pulmonary vessels. The alteration of pulmonary vessels may reflect the presence of pulmonary hypertension, as well as the extent of emphysema.

Computed tomography quantification of pulmonary vessels in chronic obstructive pulmonary disease as identified by 3D automated approach

The aim of this study was to investigate the vascular alteration of the whole lung and individual lobes in patients with COPD, and assess the association between pulmonary vessels and the extent and distribution of emphysema as well as pulmonary function by a 3-dimensional automated approach.A total of 83 computed tomography images from COPD patients were analyzed. Automated computerized approach was used to measure the total number of vessels at the fifth generation. The extent of emphysema (%LAA-950) in the whole lung and individual lobes were also calculated automatically. The association between the vascular number and the extent and distribution of emphysema, as well as the pulmonary function were assessed.Both the vascular number of fifth generation in the upper lobe and in the lower lobe were significantly negatively correlated with %LAA-950 (P < 0.05). Furthermore, there were significant, yet weak correlations between the vascular number and FEV1% predicted (R = 0.556, P = 0.039) and FEV1/FVC (R = 0.538, P = 0.047). In contrast, the vascular numbers were strongly correlated with DLco (R = 0.770, P = 0.003). Finally, the vascular number correlated closer with %LAA-950 of upper lobes than with %LAA-950 of lower lobes.Pulmonary vessel alteration can be measured; it is related to the extent of emphysema rather than the distribution of emphysema.

Novel and powerful 3D adaptive crisp active contour method applied in the segmentation of CT lung images

The World Health Organization estimates that 300 million people have asthma, 210 million people have Chronic Obstructive Pulmonary Disease (COPD), and, according to WHO, COPD will become the third major cause of death worldwide in 2030. Computational Vision systems are commonly used in pulmonology to address the task of image segmentation, which is essential for accurate medical diagnoses. Segmentation defines the regions of the lungs in CT images of the thorax that must be further analyzed by the system or by a specialist physician. This work proposes a novel and powerful technique named 3D Adaptive Crisp Active Contour Method (3D ACACM) for the segmentation of CT lung images. The method starts with a sphere within the lung to be segmented that is deformed by forces acting on it towards the lung borders. This process is performed iteratively in order to minimize an energy function associated with the 3D deformable model used. In the experimental assessment, the 3D ACACM is compared against three approaches commonly used in this field: the automatic 3D Region Growing, the level-set algorithm based on coherent propagation and the semi-automatic segmentation by an expert using the 3D OsiriX toolbox. When applied to 40 CT scans of the chest the 3D ACACM had an average F-measure of 99.22%, revealing its superiority and competency to segment lungs in CT images.

Developmental model of an automatic production of the human bronchial tree based on L-system

BACKGROUND AND OBJECTIVE

The human lungs exchange air with the external environment via the conducting airways. The application of an anatomically accurate model of the conducting airways can be helpful for simulating gas exchange and fluid distribution throughout the bronchial tree in the lung.

METHODS

In the current study, Lindenmayer system (L-system) has been formulated to generate the bronchial tree structure in a human lung. It has been considered that the structure of the bronchial tree is divided into two main segments: 1) The central airways (from the trachea to segmental bronchi) and 2) the dichotomous structure (from segmental bronchi to terminal bronchioles). Two sets of parametric rewriting rules which can be used to develop central and peripheral airways have been proposed; the first set used to develop central airways consists of seven rules, while the second rule set contains four rules.

RESULTS

The proposed model is capable of generating bronchial tree inside the volume of the host lung; and comparison of the resulting model with those reported in the literature shows that the morphometric characteristics of L-system structure are in good agreement with their corresponding experimental data.

CONCLUSION

The resulting model can be used to obtain a mathematical model required for the study of transport phenomena occurring in the lung during respiration.

Preoperative (3-dimensional) computed tomography lung reconstruction before anatomic segmentectomy or lobectomy for stage I non-small cell lung cancer

OBJECTIVES

Accurate cancer localization and negative resection margins are necessary for successful segmentectomy. In this study, we evaluate a newly developed software package that permits automated segmentation of the pulmonary parenchyma, allowing 3-dimensional assessment of tumor size, location, and estimates of surgical margins.

METHODS

A pilot study using a newly developed 3-dimensional computed tomography analytic software package was performed to retrospectively evaluate preoperative computed tomography images of patients who underwent segmentectomy (n = 36) or lobectomy (n = 15) for stage 1 non-small cell lung cancer. The software accomplishes an automated reconstruction of anatomic pulmonary segments of the lung based on bronchial arborization. Estimates of anticipated surgical margins and pulmonary segmental volume were made on the basis of 3-dimensional reconstruction.

RESULTS

Autosegmentation was achieved in 72.7% (32/44) of preoperative computed tomography images with slice thicknesses of 3 mm or less. Reasons for segmentation failure included local severe emphysema or pneumonitis, and lower computed tomography resolution. Tumor segmental localization was achieved in all autosegmented studies. The 3-dimensional computed tomography analysis provided a positive predictive value of 87% in predicting a marginal clearance greater than 1 cm and a 75% positive predictive value in predicting a margin to tumor diameter ratio greater than 1 in relation to the surgical pathology assessment.

CONCLUSIONS

This preoperative 3-dimensional computed tomography analysis of segmental anatomy can confirm the tumor location within an anatomic segment and aid in predicting surgical margins. This 3-dimensional computed tomography information may assist in the preoperative assessment regarding the suitability of segmentectomy for peripheral lung cancers.

A "loop" shape descriptor and its application to automated segmentation of airways from CT scans

PURPOSE

A novel shape descriptor is presented to aid an automated identification of the airways depicted on computed tomography (CT) images.

METHODS

Instead of simplifying the tubular characteristic of the airways as an ideal mathematical cylindrical or circular shape, the proposed "loop" shape descriptor exploits the fact that the cross sections of any tubular structure (regardless of its regularity) always appear as a loop. In implementation, the authors first reconstruct the anatomical structures in volumetric CT as a three-dimensional surface model using the classical marching cubes algorithm. Then, the loop descriptor is applied to locate the airways with a concave loop cross section. To deal with the variation of the airway walls in density as depicted on CT images, a multiple threshold strategy is proposed. A publicly available chest CT database consisting of 20 CT scans, which was designed specifically for evaluating an airway segmentation algorithm, was used for quantitative performance assessment. Measures, including length, branch count, and generations, were computed under the aid of a skeletonization operation.

RESULTS

For the test dataset, the airway length ranged from 64.6 to 429.8 cm, the generation ranged from 7 to 11, and the branch number ranged from 48 to 312. These results were comparable to the performance of the state-of-the-art algorithms validated on the same dataset.

CONCLUSIONS

The authors' quantitative experiment demonstrated the feasibility and reliability of the developed shape descriptor in identifying lung airways.

Graph-Based Airway Tree Reconstruction From Chest CT Scans: Evaluation of Different Features on Five Cohorts

We present a graph-based framework for airway tree reconstruction from computerized tomography (CT) scans and evaluate the performance of different feature categories and their combinations on five lung cohorts. The approach consists of two main processing steps. First, potential airway branch and connection candidates are identified and represented by a graph structure with weighted nodes and edges, respectively. Second, an optimization algorithm is utilized for generating an airway detection result by selecting a subset of airway branches and connections based on graph weights derived from image features. The performance of the algorithm with different feature categories and their combinations was assessed on a set of 50 lung CT scans from five different cohorts, including normal and diseased lungs. Results show trade-offs between feature categories/combinations in terms of correctly (true positive) and incorrectly (false positive) identified airways. Also, the performance of features in dependence of lung cohort was analyzed. Across all cohorts, a good trade-off with high true positive rate (TPR) and low false positive rate (FPR) was achieved by a combination of gray-value, local shape, and structural features. This combination enabled extracting 91.80% of reference airways (TPR) in combination with a low FPR of 1.00%. In addition, this variant was evaluated on the public EXACT'09 test set, and a comparison with other airway detection approaches is provided. One of the main advantages of the presented method is that it is robust against local disturbances/artifacts or other ambiguities that are frequently occurring in lung CT scans.

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 segmentation of pulmonary structures in thoracic computed tomography scans: a review

Computed tomography (CT) is the modality of choice for imaging the lungs in vivo. Sub-millimeter isotropic images of the lungs can be obtained within seconds, allowing the detection of small lesions and detailed analysis of disease processes. The high resolution of thoracic CT and the high prevalence of lung diseases require a high degree of automation in the analysis pipeline. The automated segmentation of pulmonary structures in thoracic CT has been an important research topic for over a decade now. This systematic review provides an overview of current literature. We discuss segmentation methods for the lungs, the pulmonary vasculature, the airways, including airway tree construction and airway wall segmentation, the fissures, the lobes and the pulmonary segments. For each topic, the current state of the art is summarized, and topics for future research are identified.

Bidirectional elastic image registration using B-spline affine transformation

A registration scheme termed as B-spline affine transformation (BSAT) is presented in this study to elastically align two images. We define an affine transformation instead of the traditional translation at each control point. Mathematically, BSAT is a generalized form of the affine transformation and the traditional B-spline transformation (BST). In order to improve the performance of the iterative closest point (ICP) method in registering two homologous shapes but with large deformation, a bidirectional instead of the traditional unidirectional objective/cost function is proposed. In implementation, the objective function is formulated as a sparse linear equation problem, and a sub-division strategy is used to achieve a reasonable efficiency in registration. The performance of the developed scheme was assessed using both two-dimensional (2D) synthesized dataset and three-dimensional (3D) volumetric computed tomography (CT) data. Our experiments showed that the proposed B-spline affine model could obtain reasonable registration accuracy.

Computerized identification of airway wall in CT examinations using a 3D active surface evolution approach

Airway diseases (e.g., asthma, emphysema, and chronic bronchitis) are extremely common worldwide. Any morphological variations (abnormalities) of airways may physically change airflow and ultimately affect the ability of the lungs in gas exchange. In this study, we describe a novel algorithm aimed to automatically identify airway walls depicted on CT images. The underlying idea is to place a three-dimensional (3D) surface model within airway regions and thereafter allow this model to evolve (deform) under predefined external and internal forces automatically to the location where these forces reach a state of balance. By taking advantage of the geometric and the density characteristics of airway walls, the evolution procedure is performed in a distance gradient field and ultimately stops at regions with the highest contrast. The performance of this scheme was quantitatively evaluated from several perspectives. First, we assessed the accuracy of the developed scheme using a dedicated lung phantom in airway wall estimation and compared it with the traditional full-width at half maximum (FWHM) method. The phantom study shows that the developed scheme has an error ranging from 0.04 mm to 0.36 mm, which is much smaller than the FWHM method with an error ranging from 0.16 mm to 0.84 mm. Second, we compared the results obtained by the developed scheme with those manually delineated by an experienced (>30 years) radiologist on clinical chest CT examinations, showing a mean difference of 0.084 mm. In particular, the sensitivity of the scheme to different reconstruction kernels was evaluated on real chest CT examinations. For the 'lung', 'bone' and 'standard' kernels, the average airway wall thicknesses computed by the developed scheme were 1.302 mm, 1.333 mm and 1.339 mm, respectively. Our preliminary experiments showed that the scheme had a reasonable accuracy in airway wall estimation. For a clinical chest CT examination, it took around 4 min for this scheme to identify the inner and outer airway walls on a modern PC.

Three-dimensional airway tree architecture and pulmonary function

RATIONALE AND OBJECTIVES

The airway tree is a primary conductive structure, and airways' morphologic characteristics, or variations thereof, may have an impact on airflow, thereby affecting pulmonary function. The objective of this study was to investigate the correlation between airway tree architecture, as depicted on computed tomography, and pulmonary function.

MATERIALS AND METHODS

A total of 548 chest computed tomographic examinations acquired on different patients at full inspiration were included in this study. The patients were enrolled in a study of chronic obstructive pulmonary disease (Specialized Center for Clinically Oriented Research) and underwent pulmonary function testing in addition to computed tomographic examinations. A fully automated airway tree segmentation algorithm was used to extract the three-dimensional airway tree from each examination. Using a skeletonization algorithm, airway tree volume-normalized architectural measures, including total airway length, branch count, and trachea length, were computed. Correlations between airway tree measurements with pulmonary function testing parameters and chronic obstructive pulmonary disease severity in terms of the Global Initiative for Obstructive Lung Disease classification were computed using Spearman's rank correlations.

RESULTS

Non-normalized total airway volume and trachea length were associated (P < .01) with lung capacity measures (ie, functional residual capacity, total lung capacity, inspiratory capacity, vital capacity, residual volume, and forced expiratory vital capacity). Spearman's correlation coefficients ranged from 0.27 to 0.55 (P < .01). With the exception of trachea length, all normalized architecture-based measures (ie, total airway volume, total airway length, and total branch count) had statistically significant associations with the lung function measures (forced expiratory volume in 1 second and the ratio of forced expiratory volume in 1 second to forced expiratory vital capacity), and adjusted volume was associated with all three respiratory impedance measures (lung reactance at 5 Hz, lung resistance at 5 Hz, and lung resistance at 20 Hz), and adjusted branch count was associated with all respiratory impedance measures but lung resistance at 20 Hz. When normalized for lung volume, all airway architectural measures were statistically significantly associated with chronic obstructive pulmonary disease severity, with Spearman's correlation coefficients ranging from -0.338 to -0.546 (P < .01).

CONCLUSIONS

Despite the large variability in anatomic characteristics of the airway tree across subjects, architecture-based measures demonstrated statistically significant associations (P < .01) with nearly all pulmonary function testing measures, as well as with disease severity.

Extraction of airways from CT (EXACT'09)

This paper describes a framework for establishing a reference airway tree segmentation, which was used to quantitatively evaluate fifteen different airway tree extraction algorithms in a standardized manner. Because of the sheer difficulty involved in manually constructing a complete reference standard from scratch, we propose to construct the reference using results from all algorithms that are to be evaluated. We start by subdividing each segmented airway tree into its individual branch segments. Each branch segment is then visually scored by trained observers to determine whether or not it is a correctly segmented part of the airway tree. Finally, the reference airway trees are constructed by taking the union of all correctly extracted branch segments. Fifteen airway tree extraction algorithms from different research groups are evaluated on a diverse set of twenty chest computed tomography (CT) scans of subjects ranging from healthy volunteers to patients with severe pathologies, scanned at different sites, with different CT scanner brands, models, and scanning protocols. Three performance measures covering different aspects of segmentation quality were computed for all participating algorithms. Results from the evaluation showed that no single algorithm could extract more than an average of 74% of the total length of all branches in the reference standard, indicating substantial differences between the algorithms. A fusion scheme that obtained superior results is presented, demonstrating that there is complementary information provided by the different algorithms and there is still room for further improvements in airway segmentation algorithms.

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.

Illustration of the obstacles in computerized lung segmentation using examples

PURPOSE

Automated lung volume segmentation is often a preprocessing step in quantitative lung computed tomography (CT) image analysis. The objective of this study is to identify the obstacles in computerized lung volume segmentation and illustrate those explicitly using real examples. Awareness of these "difficult" cases may be helpful for the development of a robust and consistent lung segmentation algorithm.

METHODS

We collected a large diverse dataset consisting of 2768 chest CT examinations acquired on 2292 subjects from various sources. These examinations cover a wide range of diseases, including lung cancer, chronic obstructive pulmonary disease, human immunodeficiency virus, pulmonary embolism, pneumonia, asthma, and interstitial lung disease (ILD). The CT acquisition protocols, including dose, scanners, and reconstruction kernels, vary significantly. After the application of a "neutral" thresholding-based approach to the collected CT examinations in a batch manner, the failed cases were subjectively identified and classified into different subgroups.

RESULTS

Totally, 121 failed examinations are identified, corresponding to a failure ratio of 4.4%. These failed cases are summarized as 11 different subgroups, which is further classified into 3 broad categories: (1) failure caused by diseases, (2) failure caused by anatomy variability, and (3) failure caused by external factors. The failure percentages in these categories are 62.0%, 32.2%, and 5.8%, respectively.

CONCLUSIONS

The presence of specific lung diseases (e.g., pulmonary nodules, ILD, and pneumonia) is the primary issue in computerized lung segmentation. The segmentation failures caused by external factors and anatomy variety are relatively low but unavoidable in practice. It is desirable to develop robust schemes to handle these issues in a single pass when a large number of CT examinations need to be analyzed.

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.

Synchrotron-based dynamic computed tomography of tissue motion for regional lung function measurement

During breathing, lung inflation is a dynamic process involving a balance of mechanical factors, including trans-pulmonary pressure gradients, tissue compliance and airway resistance. Current techniques lack the capacity for dynamic measurement of ventilation in vivo at sufficient spatial and temporal resolution to allow the spatio-temporal patterns of ventilation to be precisely defined. As a result, little is known of the regional dynamics of lung inflation, in either health or disease. Using fast synchrotron-based imaging (up to 60 frames s(-1)), we have combined dynamic computed tomography (CT) with cross-correlation velocimetry to measure regional time constants and expansion within the mammalian lung in vivo. Additionally, our new technique provides estimation of the airflow distribution throughout the bronchial tree during the ventilation cycle. Measurements of lung expansion and airflow in mice and rabbit pups are shown to agree with independent measures. The ability to measure lung function at a regional level will provide invaluable information for studies into normal and pathological lung dynamics, and may provide new pathways for diagnosis of regional lung diseases. Although proof-of-concept data were acquired on a synchrotron, the methodology developed potentially lends itself to clinical CT scanning and therefore offers translational research opportunities.