A Computational geometry approach to automated pulmonary fissure segmentation in CT examinations

Quantitative assessment of lung structure changes in low-intensity smokers: a retrospective study in a Chinese male cohort

Background

With an increasing number of smokers who consume fewer cigarettes, it is crucial to understand the lung structure changes of low-intensity smoking. This study aimed to investigate the lung structure changes in low-intensity smokers in a Chinese male cohort.

Methods

Chest computed tomography (CT) examinations of 465 asymptomatic healthy male participants were divided into non-smoking (n=256), light-smoking (n=84), intermediate-smoking (n=85), and heavy-smoking (n=40) groups. Low-intensity smokers (fewer than 10 cigarettes per day) were included (n=32), and a new group of non-smokers was generated using propensity score matching according to age. Quantitative CT parameters, including the volume of the intrapulmonary vessel (IPVV), the volume of the lung, mean lung density (MLD), the low-attenuation areas below -910 Hounsfield units (LAA-910), and the volume ratio of intrapulmonary vessel to the lung for the total lung and each lobe were measured. Quantitative CT parameters were compared among the four smoking groups and also between the low-intensity smokers and non-smokers. Binary logistic regression was used to determine the independent quantitative CT measurements of smoking intensity.

Results

Compared with that in non-smokers, the IPVV and the MLD of the total lung and five lobes was significantly higher in light smokers (P<0.05); meanwhile, the LAA-910 of the total lung and five lobes of the light and intermediate smokers were significantly lower (P<0.05). The IPVV of the total lung and five lobes was significantly higher in the low-intensity smoking group (P<0.05). The IPVV of the total lung was the independent factor for discriminating between the non-smokers and light smokers (odds ratio =1.040; 95% confidence interval: 1.027-1.053) and between the non-smokers and low-intensity smokers (odds ratio =1.034; 95% confidence interval: 1.013-1.055).

Conclusions

CT-quantified measurements of the IPVVs and MLD increased in light and intermediate smokers. The IPVV of the total lung was selected as the independent factor between non-smokers and light smokers and between non-smokers and low-intensity smokers.

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.

Quantification of pulmonary vessel volumes on low-dose computed tomography in a healthy male Chinese population: the effects of aging and smoking

BACKGROUND

This study sought to determine pulmonary vascular volumes (PVVs) on low-dose computed tomography (LDCT) in a healthy male Chinese population and analyze the effects of aging and smoking on PVVs.

METHODS

A total of 1,320 healthy male participants (comprising 720 non-smokers, 445 smokers, and 155 ex-smokers) who underwent LDCT were retrospectively included in this study. Their demographic data and smoking status data were collected. An automatic integration segmentation approach for LDCT was used to segment pulmonary vessels semi-automatically. The PVVs of the whole lung, left lung, and right lung on LDCT were calculated, and correlations between PVVs and age and smoking status were then compared.

RESULTS

The inter-rater correlation coefficient of the whole lung, left lung, and right lung PVVs was 0.98 [95% confidence interval (CI): 0.95-0.99], 0.97 (95% CI: 0.93-0.98), and 0.97 (95% CI: 0.94-0.99), respectively. The intra-class correlation coefficient of the whole lung left lung, and right lung PVVs was 0.98 (95% CI: 0.95-0.99), 0.96 (95% CI: 0.95-0.99), and 0.96 (95% CI: 0.92-0.98), respectively. In non-smokers, PVVs decreased with age. The PVVs of heavy smokers were higher than those of light smokers, ex-smokers, and non-smokers. The PVVs of ex-smokers were comparable to those of light smokers.

CONCLUSIONS

The PVVs measured on LDCT tended to decrease with age in healthy male non-smokers gradually. Compared to non-smokers, the PVVs of smokers increased, even with the normal lung function.

Potential Value of Expiratory CT in Quantitative Assessment of Pulmonary Vessels in COPD

Objective: To investigate the associations between intrapulmonary vascular volume (IPVV) depicted on inspiratory and expiratory CT scans and disease severity in COPD patients, and to determine which CT parameters can be used to predict IPVV.

Methods: We retrospectively collected 89 CT examinations acquired on COPD patients from an available database. All subjects underwent both inspiratory and expiratory CT scans. We quantified the IPVV, airway wall thickness (WT), the percentage of the airway wall area (WA%), and the extent of emphysema (LAA%₋₉₅₀) using an available pulmonary image analysis tool. The underlying relationship between IPVV and COPD severity, which was defined as mild COPD (GOLD stage I and II) and severe COPD (GOLD stage III and IV), was analyzed using the Student's t-test (or Mann-Whitney U-test). The correlations of IPVV with pulmonary function tests (PFTs), LAA%₋₉₅₀, and airway parameters for the third to sixth generation bronchus were analyzed using the Pearson or Spearman's rank correlation coefficients and multiple stepwise regression.

Results: In the subgroup with only inspiratory examinations, the correlation coefficients between IPVV and PFT measures were −0.215 ~ −0.292 (p < 0.05), the correlation coefficients between IPVV and WT₃₋₆ were 0.233 ~ 0.557 (p < 0.05), and the correlation coefficient between IPVV and LAA%₋₉₅₀ were 0.238 ~ 0.409 (p < 0.05). In the subgroup with only expiratory scan, the correlation coefficients between IPVV and PFT measures were −0.238 ~ −0.360 (p < 0.05), the correlation coefficients between IPVV and WT₃₋₆ were 0.260 ~ 0.566 (p < 0.05), and the correlation coefficient between IPVV and LAA%₋₉₅₀ were 0.241 ~ 0.362 (p < 0.05). The multiple stepwise regression analyses demonstrated that WT were independently associated with IPVV (P < 0.05).

Conclusion: The expiratory CT scans can provide a more accurate assessment of COPD than the inspiratory CT scans, and the airway wall thickness maybe an independent predictor of pulmonary vascular alteration in patients with COPD.

IoT based lung cancer detection using machine learning and cuckoo search optimization

Purpose

Detecting cancer from the computed tomography (CT)images of lung nodules is very challenging for radiologists. Early detection of cancer helps to provide better treatment in advance and to enhance the recovery rate. Although a lot of research is being carried out to process clinical images, it still requires improvement to attain high reliability and accuracy. The main purpose of this paper is to achieve high accuracy in detecting and classifying the lung cancer and assisting the radiologists to detect cancer by using CT images. The CT images are collected from health-care centres and remote places through Internet of Things (IoT)-enabled platform and the image processing is carried out in the cloud servers.

Design/methodology/approach

IoT-based lung cancer detection is proposed to access the lung CT images from any remote place and to provide high accuracy in image processing. Here, the exact separation of lung nodule is performed by Otsu thresholding segmentation with the help of optimal characteristics and cuckoo search algorithm. The important features of the lung nodules are extracted by local binary pattern. From the extracted features, support vector machine (SVM) classifier is trained to recognize whether the lung nodule is malicious or non-malicious.

Findings

The proposed framework achieves 99.59% in accuracy, 99.31% in sensitivity and 71% in peak signal to noise ratio. The outcomes show that the proposed method has achieved high accuracy than other conventional methods in early detection of lung cancer.

Practical implications

The proposed algorithm is implemented and tested by using more than 500 images which are collected from public and private databases. The proposed research framework can be used to implement contextual diagnostic analysis.

Originality/value

The cancer nodules in CT images are precisely segmented by integrating the algorithms of cuckoo search and Otsu thresholding in order to classify malicious and non-malicious nodules.

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.

Lung Lobe Segmentation Based on Lung Fissure Surface Classification Using a Point Cloud Region Growing Approach

In anatomy, the lung can be divided by lung fissures into several pulmonary lobe units with specific functions. Identifying the lung lobes and the distribution of various diseases among different lung lobes from CT images is important for disease diagnosis and tracking after recovery. In order to solve the problems of low tubular structure segmentation accuracy and long algorithm time in segmenting lung lobes based on lung anatomical structure information, we propose a segmentation algorithm based on lung fissure surface classification using a point cloud region growing approach. We cluster the pulmonary fissures, transformed into point cloud data, according to the differences in the pulmonary fissure surface normal vector and curvature estimated by principal component analysis. Then, a multistage spline surface fitting method is used to fill and expand the lung fissure surface to realize the lung lobe segmentation. The proposed approach was qualitatively and quantitatively evaluated on a public dataset from Lobe and Lung Analysis 2011 (LOLA11), and obtained an overall score of 0.84. Although our approach achieved a slightly lower overall score compared to the deep learning based methods (LobeNet_V2 and V-net), the inter-lobe boundaries from our approach were more accurate for the CT images with visible lung fissures.

Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans

Pulmonary lobe segmentation in computed tomography scans is essential for regional assessment of pulmonary diseases. Recent works based on convolution neural networks have achieved good performance for this task. However, they are still limited in capturing structured relationships due to the nature of convolution. The shape of the pulmonary lobes affect each other and their borders relate to the appearance of other structures, such as vessels, airways, and the pleural wall. We argue that such structural relationships play a critical role in the accurate delineation of pulmonary lobes when the lungs are affected by diseases such as COVID-19 or COPD. In this paper, we propose a relational approach (RTSU-Net) that leverages structured relationships by introducing a novel non-local neural network module. The proposed module learns both visual and geometric relationships among all convolution features to produce self-attention weights. With a limited amount of training data available from COVID-19 subjects, we initially train and validate RTSU-Net on a cohort of 5000 subjects from the COPDGene study (4000 for training and 1000 for evaluation). Using models pre-trained on COPDGene, we apply transfer learning to retrain and evaluate RTSU-Net on 470 COVID-19 suspects (370 for retraining and 100 for evaluation). Experimental results show that RTSU-Net outperforms three baselines and performs robustly on cases with severe lung infection due to COVID-19.

Optimal threshold in low-dose CT quantification of emphysema

OBJECTIVE

Low-dose CT is now widely used in the screening of lung cancer and the detection of pulmonary nodules. There has also been increasing interest in using Low-dose CT for evaluating emphysema. In conventional dose CT, the threshold of -950HU is a common threshold for density-based emphysema quantification for worldwide population. However, the optimal threshold for assessing emphysema at low-dose CT has not been determined. The purpose of this study is to determine the optimal threshold for low-dose CT quantification of emphysema for Chinese population.

MATERIALS AND METHODS

In this study, 548 low-dose chest CT examinations acquired from different subjects (119 none, 49 mild, 163 moderate, 152 severe, and 65 very severe obstruction) are collected. At the level of the entire lung and individual lobes, the extent of emphysema was quantified by the percentage of the low attenuation area (LAA%) at a wide range of thresholds from -850HU to -1000HU. Both Pearson and Spearman's rank correlation coefficients were used to assess the correlations between 1) LAA% and pulmonary functions and 2) LAA% and the five-category classification. The statistical significance of the difference between correlation coefficients were evaluated using Steiger'Z test.

RESULTS

LAA% had a good correlation with both pulmonary function (|r| = 0.1-0.600, p < 0.001) and the five-category classification (r = 0.163-0.602, p < 0.001) in both the entire lung and individual lobes under different thresholds. The highest correlation coefficient is obtained at -940HU instead of -950HU.

CONCLUSION

Low-dose CT can be used for quantitative assessment of emphysema, and the threshold of -940HU is a suitable threshold for quantifying emphysema in low-dose CT images for Chinese population.

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.

An Efficient Method for the Detection of Oblique Fissures from Computed Tomography images of Lungs

Detection of a pulmonary fissure in lungs is difficult due to its anatomical changeability among humans and it is essential in the clinical environment for accurate localizing and treating the lung abnormalities on a lobe level in human lungs. In this work, an algorithmic approach is proposed to detect the lung oblique fissures from lung computed tomography (CT) images. In the preprocessing module of our approach, the lung structures are enhanced using morphological operation and lung images are de-noised using Wiener filter. In the second module, lung regions are segmented using techniques, namely, thresholding and background subtraction. In the third module of our algorithm, initially, fissure regions are segmented using the active contour model, then by applying the rule based approach on the fissure regions, the oblique fissures are segmented. The proposed algorithm has been tested on 50 images collected from Lung Image Database Consortium (LIDC) and 30 images obtained from Early Lung Cancer Action Program (ELCAP).

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.

Limitations of an automated embolism segmentation method in clinical practice

PURPOSE

Automated pulmonary embolism (PE) segmentation is frequently used as a preprocessing step in the quantitative analysis of pulmonary embolism. Objective of this study is to analyze the potential limitation in automated PE segmentation using clinical cases.

METHODS

A database of 304 computer tomography pulmonary angiography (CTPA) examinations was collected and confirmed to be PE. After processing using an automated scheme, two radiologists classified these cases into four groups of A, B, C and D, which represent 4 different segmentation results namely, (1) entire pulmonary artery identified without motivation artifacts, (2) entire pulmonary artery identified with motivation artifacts, (3) part of the pulmonary artery identified, and (4) none of the pulmonary artery identified. Then, the possible failed reasons in PE segmentation were analyzed and determined based on the image characterization of the diseases and the applied CTPA scanning protocols.

RESULTS

In the study, 143 (47.0%., 30 (9.9%., 110 (36.2%. and 21 (6.9%. examinations were classified into groups A, B, C and D, respectively. Group C and D included the cases with failed segmentation. Fifteen failure reasons, including intrapulmonary abnormalities, extra-pulmonary abnormalities, diffuse pulmonary diseases, enlarged heart, absolute occluded vessels, embolism attached to artery wall, delayed scan time, skewed location, low scan dose, obvious artifact of superior vena cava, previous chest surgery, congenital deformities of the chest, incorrect positioning, missed images and other unknown reasons, were determined with corresponding case percentages ranging from 0.3%.o 9.2%.

CONCLUSIONS

Automated segmentation failures were caused by specific lung diseases, anatomy varieties, improper scan time, improper scan dose, manual errors or other unknown reasons. Realization of those limitations is crucial for developing robust automated schemes to handle these issues in a single pass when a large number of CTPA examinations need to be analyzed.

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.

Rationale and Design of the Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis (GRADS) Study. Sarcoidosis Protocol

Sarcoidosis is a systemic disease characterized by noncaseating granulomatous inflammation with tremendous clinical heterogeneity and uncertain pathobiology and lacking in clinically useful biomarkers. The Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis (GRADS) study is an observational cohort study designed to explore the role of the lung microbiome and genome in these two diseases. This article describes the design and rationale for the GRADS study sarcoidosis protocol. The study addresses the hypothesis that distinct patterns in the lung microbiome are characteristic of sarcoidosis phenotypes and are reflected in changes in systemic inflammatory responses as measured by peripheral blood changes in gene transcription. The goal is to enroll 400 participants, with a minimum of 35 in each of 9 clinical phenotype subgroups prioritized by their clinical relevance to understanding of the pathobiology and clinical heterogeneity of sarcoidosis. Participants with a confirmed diagnosis of sarcoidosis undergo a baseline visit with self-administered questionnaires, chest computed tomography, pulmonary function tests, and blood and urine testing. A research or clinical bronchoscopy with a research bronchoalveolar lavage will be performed to obtain samples for genomic and microbiome analyses. Comparisons will be made by blood genomic analysis and with clinical phenotypic variables. A 6-month follow-up visit is planned to assess each participant's clinical course. By the use of an integrative approach to the analysis of the microbiome and genome in selected clinical phenotypes, the GRADS study is powerfully positioned to inform and direct studies on the pathobiology of sarcoidosis, identify diagnostic or prognostic biomarkers, and provide novel molecular phenotypes that could lead to improved personalized approaches to therapy for sarcoidosis.

Pulmonary Fissure Detection in CT Images Using a Derivative of Stick Filter

Pulmonary fissures are important landmarks for recognition of lung anatomy. In CT images, automatic detection of fissures is complicated by factors like intensity variability, pathological deformation and imaging noise. To circumvent this problem, we propose a derivative of stick (DoS) filter for fissure enhancement and a post-processing pipeline for subsequent segmentation. Considering a typical thin curvilinear shape of fissure profiles inside 2D cross-sections, the DoS filter is presented by first defining nonlinear derivatives along a triple stick kernel in varying directions. Then, to accommodate pathological abnormality and orientational deviation, a [Formula: see text] cascading and multiple plane integration scheme is adopted to form a shape-tuned likelihood for 3D surface patches discrimination. During the post-processing stage, our main contribution is to isolate the fissure patches from adhering clutters by introducing a branch-point removal algorithm, and a multi-threshold merging framework is employed to compensate for local intensity inhomogeneity. The performance of our method was validated in experiments with two clinical CT data sets including 55 publicly available LOLA11 scans as well as separate left and right lung images from 23 GLUCOLD scans of COPD patients. Compared with manually delineating interlobar boundary references, our method obtained a high segmentation accuracy with median F1-scores of 0.833, 0.885, and 0.856 for the LOLA11, left and right lung images respectively, whereas the corresponding indices for a conventional Wiemker filtering method were 0.687, 0.853, and 0.841. The good performance of our proposed method was also verified by visual inspection and demonstration on abnormal and pathological cases, where typical deformations were robustly detected together with normal fissures.

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.

Automated compromised right lung segmentation method using a robust atlas-based active volume model with sparse shape composition prior in CT

To resolve challenges in image segmentation in oncologic patients with severely compromised lung, we propose an automated right lung segmentation framework that uses a robust, atlas-based active volume model with a sparse shape composition prior. The robust atlas is achieved by combining the atlas with the output of sparse shape composition. Thoracic computed tomography images (n=38) from patients with lung tumors were collected. The right lung in each scan was manually segmented to build a reference training dataset against which the performance of the automated segmentation method was assessed. The quantitative results of this proposed segmentation method with sparse shape composition achieved mean Dice similarity coefficient (DSC) of (0.72, 0.81) with 95% CI, mean accuracy (ACC) of (0.97, 0.98) with 95% CI, and mean relative error (RE) of (0.46, 0.74) with 95% CI. Both qualitative and quantitative comparisons suggest that this proposed method can achieve better segmentation accuracy with less variance than other atlas-based segmentation methods in the compromised lung segmentation.

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.

Impact of emphysema heterogeneity on pulmonary function

Objectives

To investigate the association between emphysema heterogeneity in spatial distribution, pulmonary function and disease severity.

Methods and Materials

We ascertained a dataset of anonymized Computed Tomography (CT) examinations acquired on 565 participants in a COPD study. Subjects with chronic bronchitis (CB) and/or bronchodilator response were excluded resulting in 190 cases without COPD and 160 cases with COPD. Low attenuations areas (LAAs) (≤950 Hounsfield Unit (HU)) were identified and quantified at the level of individual lobes. Emphysema heterogeneity was defined in a manner that ranged in value from −100% to 100%. The association between emphysema heterogeneity and pulmonary function measures (e.g., FEV1% predicted, RV/TLC, and DLco% predicted) adjusted for age, sex, and smoking history (pack-years) was assessed using multiple linear regression analysis.

Results

The majority (128/160) of the subjects with COPD had a heterogeneity greater than zero. After adjusting for age, gender, smoking history, and extent of emphysema, heterogeneity in depicted disease in upper lobe dominant cases was positively associated with pulmonary function measures, such as FEV1 Predicted (p<.001) and FEV1/FVC (p<.001), as well as disease severity (p<0.05). We found a negative association between HI% , RV/TLC (p<0.001), and DLco% (albeit not a statistically significant one, p = 0.06) in this group of patients.

Conclusion

Subjects with more homogeneous distribution of emphysema and/or lower lung dominant emphysema tend to have worse pulmonary function.

Pulmonary lobe segmentation based on ridge surface sampling and shape model fitting

PURPOSE

Performing lobe-based quantitative analysis of the lung in computed tomography (CT) scans can assist in efforts to better characterize complex diseases such as chronic obstructive pulmonary disease (COPD). While airways and vessels can help to indicate the location of lobe boundaries, segmentations of these structures are not always available, so methods to define the lobes in the absence of these structures are desirable.

METHODS

The authors present a fully automatic lung lobe segmentation algorithm that is effective in volumetric inspiratory and expiratory computed tomography (CT) datasets. The authors rely on ridge surface image features indicating fissure locations and a novel approach to modeling shape variation in the surfaces defining the lobe boundaries. The authors employ a particle system that efficiently samples ridge surfaces in the image domain and provides a set of candidate fissure locations based on the Hessian matrix. Following this, lobe boundary shape models generated from principal component analysis (PCA) are fit to the particles data to discriminate between fissure and nonfissure candidates. The resulting set of particle points are used to fit thin plate spline (TPS) interpolating surfaces to form the final boundaries between the lung lobes.

RESULTS

The authors tested algorithm performance on 50 inspiratory and 50 expiratory CT scans taken from the COPDGene study. Results indicate that the authors' algorithm performs comparably to pulmonologist-generated lung lobe segmentations and can produce good results in cases with accessory fissures, incomplete fissures, advanced emphysema, and low dose acquisition protocols. Dice scores indicate that only 29 out of 500 (5.85%) lobes showed Dice scores lower than 0.9. Two different approaches for evaluating lobe boundary surface discrepancies were applied and indicate that algorithm boundary identification is most accurate in the vicinity of fissures detectable on CT.

CONCLUSIONS

The proposed algorithm is effective for lung lobe segmentation in absence of auxiliary structures such as vessels and airways. The most challenging cases are those with mostly incomplete, absent, or near-absent fissures and in cases with poorly revealed fissures due to high image noise. However, the authors observe good performance even in the majority of these cases.

A hybrid approach to segmentation of diseased lung lobes

Complete segmentation of diseased lung lobes by automatically identifying fissure surfaces is a nontrivial task, due to incomplete, disrupted, and deformed fissures. In this paper, we present a novel algorithm employing a hybrid two-dimensional/three-dimensional approach for segmenting diseased lung lobes. Our approach models complete fissure surfaces from partial fissures found in individual computed tomography (CT) images. Evaluated using 24 patients' lungs with a variety of different diseases, our algorithm produced root-mean square errors of 2.21 ± 1.21, 2.51 ± 1.36, and 2.38 ± 1.27 mm for segmenting the left oblique fissure (LOF), right oblique fissure (ROF) and right horizontal fissure (RHF), respectively. The average accuracies for segmenting the LOF, ROF, and RHF are 86.59%, 84.80%, and 82.62%, using our ±3-mm percentile measure. These results indicate the feasibility of developing an automatic algorithm for complete segmentation of diseased lung lobes.

Fast and adaptive detection of pulmonary nodules in thoracic CT images using a hierarchical vector quantization scheme

Computer-aided detection (CADe) of pulmonary nodules is critical to assisting radiologists in early identification of lung cancer from computed tomography (CT) scans. This paper proposes a novel CADe system based on a hierarchical vector quantization (VQ) scheme. Compared with the commonly-used simple thresholding approach, the high-level VQ yields a more accurate segmentation of the lungs from the chest volume. In identifying initial nodule candidates (INCs) within the lungs, the low-level VQ proves to be effective for INCs detection and segmentation, as well as computationally efficient compared to existing approaches. False-positive (FP) reduction is conducted via rule-based filtering operations in combination with a feature-based support vector machine classifier. The proposed system was validated on 205 patient cases from the publically available online Lung Image Database Consortium database, with each case having at least one juxta-pleural nodule annotation. Experimental results demonstrated that our CADe system obtained an overall sensitivity of 82.7% at a specificity of 4 FPs/scan. Especially for the performance on juxta-pleural nodules, we observed 89.2% sensitivity at 4.14 FPs/scan. With respect to comparable CADe systems, the proposed system shows outperformance and demonstrates its potential for fast and adaptive detection of pulmonary nodules via CT imaging.

Pulmonary fissure integrity and collateral ventilation in COPD patients

Purpose

To investigate whether the integrity (completeness) of pulmonary fissures affects pulmonary function in patients with chronic obstructive pulmonary disease (COPD).

Materials and Methods

A dataset consisting of 573 CT exams acquired on different subjects was collected from a COPD study. According to the global initiative for chronic obstructive lung disease (GOLD) criteria, these subjects (examinations) were classified into five different subgroups, namely non-COPD (222 subjects), GOLD-I (83 subjects), GOLD-II (141 subjects), GOLD-III (63 subjects), and GOLD-IV (64 subjects), in terms of disease severity. An available computer tool was used to aid in an objective and efficient quantification of fissure integrity. The correlations between fissure integrity, and pulmonary functions (e.g., FEV1, and FEV1/FVC) and COPD severity were assessed using Pearson and Spearman's correlation coefficients, respectively.

Results

For the five sub-groups ranging from non-COPD to GOLD-IV, the average integrities of the right oblique fissure (ROF) were 81.8%, 82.4%, 81.8%, 82.8%, and 80.2%, respectively; the average integrities of the right horizontal fissure (RHF) were 62.6%, 61.8%, 62.1%, 62.2%, and 62.3%, respectively; the average integrities of the left oblique fissure (LOF) were 82.0%, 83.2%, 81.7%, 82.0%, and 78.4%, respectively; and the average integrities of all fissures in the entire lung were 78.0%, 78.6%, 78.1%, 78.5%, and 76.4%, respectively. Their Pearson correlation coefficients with FEV1 and FE1/FVC range from 0.027 to 0.248 with p values larger than 0.05. Their Spearman correlation coefficients with COPD severity except GOLD-IV range from −0.013 to −0.073 with p values larger than 0.08.

Conclusion

There is no significant difference in fissure integrity for patients with different levels of disease severity, suggesting that the development of COPD does not change the completeness of pulmonary fissures and incomplete fissures alone may not contribute to the collateral ventilation.

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.

Automatic segmentation of pulmonary fissures in computed tomography images using 3D surface features

Pulmonary interlobar fissures are important anatomic structures in human lungs and are useful in locating and classifying lung abnormalities. Automatic segmentation of fissures is a difficult task because of their low contrast and large variability. We developed a fully automatic training-free approach for fissure segmentation based on the local bending degree (LBD) and the maximum bending index (MBI). The LBD is determined by the angle between the eigenvectors of two Hessian matrices for a pair of adjacent voxels. It is used to construct a constraint to extract the candidate surfaces in three-dimensional (3D) space. The MBI is a measure to discriminate cylindrical surfaces from planar surfaces in 3D space. Our approach for segmenting fissures consists of five steps, including lung segmentation, plane-like structure enhancement, surface extraction with LBD, initial fissure identification with MBI, and fissure extension based on local plane fitting. When applying our approach to 15 chest computed tomography (CT) scans, the mean values of the positive predictive value, the sensitivity, the root-mean square (RMS) distance, and the maximal RMS are 91 %, 88 %, 1.01 ± 0.99 mm, and 11.56 mm, respectively, which suggests that our algorithm can efficiently segment fissures in chest CT scans.

Assessment of lung volume collapsibility in chronic obstructive lung disease patients using CT

OBJECTIVE

To investigate the collapsibility of the lung and individual lobes in patients with COPD during inspiration/expiration and assess the association of whole lung and lobar volume changes with pulmonary function tests (PFTs) and disease severity.

METHODS

PFT measures used were RV/TLC%, FEV1% predicted, FVC, FEV1/FVC%, DLco% predicted and GOLD category. A total of 360 paired inspiratory and expiratory CT examinations acquired in 180 subjects were analysed. Automated computerised algorithms were used to compute individual lobe and total lung volumes. Lung volume collapsibility was assessed quantitatively using the simple difference between CT computed inspiration (I) and expiration (E) volumes (I-E), and a relative measure of volume changes, (I-E)/I.

RESULTS

Mean absolute collapsibility (I-E) decreased in all lung lobes with increasing disease severity defined by GOLD classification. Relative collapsibility (I-E)/I showed a similar trend. Upper lobes had lower volume collapsibility across all GOLD categories and lower lobes collectively had the largest volume collapsibility. Whole lung and left lower lobe collapsibility measures tended to have the highest correlations with PFT measures. Collapsibility of lung lobes and whole lung was also negatively correlated with the degree of air trapping between expiration and inspiration, as measured by mean lung density. All measured associations were statistically significant (P < 0.01).

CONCLUSION

Severity of COPD appears associated with increased collapsibility in the upper lobes, but change (decline) in collapsibility is faster in the lower lobes.

KEY POINTS

• Inspiratory and expiratory computed tomography allows assessment of lung collapsibility • Lobe volume collapsibility is significantly correlated with measures of lung function. • As COPD severity increases, collapsibility of individual lung lobes decreases. • Upper lobes exhibit more severe disease, while lower lobes decline faster.

Automatic segmentation of the pulmonary lobes from chest CT scans based on fissures, vessels, and bronchi

Segmentation of the pulmonary lobes is relevant in clinical practice and particularly challenging for cases with severe diseases or incomplete fissures. In this work, an automated segmentation approach is presented that performs a marker-based watershed transformation on computed tomography (CT) scans to subdivide the lungs into lobes. A cost image for the watershed transformation is computed by combining information from fissures, bronchi, and pulmonary vessels. The lobar markers are calculated by an analysis of the automatically labeled bronchial tree. By integration of information from several anatomical structures the segmentation is made robust against incomplete fissures. For evaluation the method was compared to a recently published method on 20 CT scans with no or mild disease. The average distances to the reference segmentation were 0.69, 0.67, and 1.21 mm for the left major, right major, and right minor fissure, respectively. In addition the results were submitted to LOLA11, an international lung lobe segmentation challenge with publically available data including cases with severe diseases. The average distances to the reference for the 55 CT scans provided by LOLA11 were 0.98, 3.97, and 3.09 mm for the left major, right major, and right minor fissure. Moreover, an analysis of the relation between segmentation quality and fissure completeness showed that the method is robust against incomplete fissures.

Computer-aided diagnosis systems for lung cancer: challenges and methodologies

This paper overviews one of the most important, interesting, and challenging problems in oncology, the problem of lung cancer diagnosis. Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient's chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant. This paper overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps. For each technique, various aspects of technical issues, implemented methodologies, training and testing databases, and validation methods, as well as achieved performances, are described. In addition, the paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems.

Optimal threshold in CT quantification of emphysema

OBJECTIVES

To determine the optimal threshold by quantitatively assessing the extent of emphysema at the level of the entire lung and at the level of individual lobes using a large, diverse dataset of computed tomography (CT) examinations.

METHODS

This study comprises 573 chest CT examinations acquired from subjects with different levels of airway obstruction (222 none, 83 mild, 141 moderate, 63 severe and 64 very severe). The extent of emphysema was quantified using the percentage of the low attenuation area (LAA%) divided by the total lung or lobe volume(s). The correlations between the extent of emphysema, and pulmonary functions and the five-category classification were assessed using Pearson and Spearman's correlation coefficients, respectively. When quantifying emphysema using a density mask, a wide range of thresholds from -850 to -1,000 HU were used.

RESULTS

The highest correlations of LAA% with the five-category classification and PFT measures ranged from -925 to -965 HU for each individual lobe and the entire lung. However, the differences between the highest correlations and those obtained at -950 HU are relatively small.

CONCLUSION

Although there are variations in the optimal cut-off thresholds for individual lobes, the single threshold of -950 HU is still an acceptable threshold for density-based emphysema quantification.

Potential of computer-aided diagnosis to improve CT lung cancer screening

The development of low-dose spiral computed tomography (CT) has rekindled hope that effective lung cancer screening might yet be found. Screening is justified when there is evidence that it will extend lives at reasonable cost and acceptable levels of risk. A screening test should detect all extant cancers while avoiding unnecessary workups. Thus optimal screening modalities have both high sensitivity and specificity. Due to the present state of technology, radiologists must opt to increase sensitivity and rely on follow-up diagnostic procedures to rule out the incurred false positives. There is evidence in published reports that computer-aided diagnosis technology may help radiologists alter the benefit-cost calculus of CT sensitivity and specificity in lung cancer screening protocols. This review will provide insight into the current discussion of the effectiveness of lung cancer screening and assesses the potential of state-of-the-art computer-aided design developments.

Identification of pulmonary fissures using a piecewise plane fitting algorithm

We describe an automated computerized scheme to identify pulmonary fissures depicted in chest computed tomography (CT) examinations from a novel perspective. Whereas CT images can be regarded as a cloud of points, the underlying idea is to search for surface-like structures in the three-dimensional (3D) Euclidean space by using an efficient plane fitting algorithm. The proposed plane fitting operation is performed in a number of small spherical lung sub-volumes to detect small planar patches. Using a simple clustering criterion based on their spatial coherence and surface area, the identified planar patches, assumed to represent fissures, are classified into different types of fissures, namely left oblique, right oblique and right horizontal fissures. The performance of the developed scheme was assessed by comparing with a manually created "reference standard" and the results obtained by a previously developed approach on a dataset of 30 lung CT examinations. The experiments show that the average discrepancy is around 1.0mm in comparison with the reference standard, while the corresponding maximum discrepancy is 20.5mm. In addition, 94% of the fissure voxels identified by the computerized scheme are within 3mm of the fissures in the reference standard. As compared to a previously developed approach, we also found that the newly developed scheme had a smaller discrepancy with the standard reference. In efficiency, it takes approximately 8 min to identify the fissures in a chest CT examination on a typical PC. The developed scheme demonstrates a reasonable performance in terms of accuracy, robustness, and computational efficiency.

Pulmonary kinematics from image data: a review

The effects of certain lung pathologies include alterations in lung physiology negatively affecting pulmonary compliance. Current approaches to diagnosis and treatment assessment of lung disease commonly rely on pulmonary function testing. Such testing is limited to global measures of lung function, neglecting regional measurements, which are critical for early diagnosis and localization of disease. Increased accessibility to medical image acquisition strategies with high spatiotemporal resolution coupled with the development of sophisticated intensity-based and geometric registration techniques has resulted in the recent exploration of modeling pulmonary motion for calculating local measures of deformation. In this review, the authors provide a broad overview of such research efforts for the estimation of pulmonary deformation. This includes discussion of various techniques, current trends in validation approaches, and the public availability of software and data resources.

A differential geometric approach to automated segmentation of human airway tree

Airway diseases are frequently associated with morphological changes that may affect the physiology of the lungs. Accurate characterization of airways may be useful for quantitatively assessing prognosis and for monitoring therapeutic efficacy. The information gained may also provide insight into the underlying mechanisms of various lung diseases. We developed a computerized scheme to automatically segment the 3-D human airway tree depicted on computed tomography (CT) images. The method takes advantage of both principal curvatures and principal directions in differentiating airways from other tissues in geometric space. A "puzzle game" procedure is used to identify false negative regions and reduce false positive regions that do not meet the shape analysis criteria. The negative impact of partial volume effects on small airway detection is partially alleviated by repeating the developed differential geometric analysis on lung anatomical structures modeled at multiple iso-values (thresholds). In addition to having advantages, such as full automation, easy implementation and relative insensitivity to image noise and/or artifacts, this scheme has virtually no leakage issues and can be easily extended to the extraction or the segmentation of other tubular type structures (e.g., vascular tree). The performance of this scheme was assessed quantitatively using 75 chest CT examinations acquired on 45 subjects with different slice thicknesses and using 20 publicly available test cases that were originally designed for evaluating the performance of different airway tree segmentation algorithms.

Identification of lobar fissures in pathological lungs

Identification of lobar fissures in human lungs is a non-trivial task due to their variable shape and appearance, along with the low contrast and high noise in computed tomographic (CT) images. Pathologies in the lungs can further complicate this identification by deforming and/or disrupting the lobar fissures. Current algorithms rely on the general anatomy of the lungs to find fissures affected by pathologies. This can be unreliable as deformations and/or disruptions of these fissures will alter the general lung anatomy. To overcome this, we developed an algorithm with the following novelties: (1) a new application of neural network based texture analysis to generalize fissure regions; and (2) a new method of fissure surface identification. We tested our algorithm on CT image stacks from 8 anonymous patients with pathological lungs. Compared to manually segmented fissures, our algorithm produced an average mean difference of 0.71 mm and 0.68 mm for identifying the left and right oblique fissures, respectively. Using a 3-mm percentile measure, the algorithm yielded an average accuracy of 86.8% for the left oblique fissure with a mean worst-case error of 3.18 mm. For the right oblique fissure, the algorithm produced an accuracy of 88.8% with a mean worst-case error of 3.13 mm. The above results show feasibility of using our algorithm for identifying fissures in pathological lungs.

Computerized assessment of pulmonary fissure integrity using high resolution CT

PURPOSE

Knowledge of pulmonary interlobar fissure integrity is of interest in a number of clinical and investigational applications. The authors developed and tested a high resolution CT based automated computerized scheme for this purpose.

METHODS

The fissure integrity assessment scheme consists of the following steps: (1) Fissure detection, (2) individual fissure identification, (3) fissure type determination, (4) "complete" interlobe surface estimation, and (5) fissure integrity estimation. For evaluation purposes, 50 anonymized chest CT examinations were ascertained and the complete and "incomplete" regions of the fissures of interest were manually marked by two experienced radiologists. After applying the scheme to the same examinations, differences among fissure percent completeness estimates based on the radiologists' manual markings and the automated computerized scheme were computed and compared.

RESULTS

Average differences in estimated fissure percent completeness (integrity) between the results of the computerized scheme and that based on each of the two radiologists' markings were 6.88% +/- 5.86%, 9.57% +/- 7.77%, and 4.19% +/- 5.64% for the right major fissures, the right minor fissures, and the left major fissures, respectively. The differences between results based on radiologists' markings for the same fissures were 4.27% +/- 3.32%, 7.02% +/- 5.54%, and 4.23% +/- 4.93%, respectively. The difference among the three matched measurement sets for each fissure were statistically significant (Friedman's test, p < or = 0.005) but paired comparisons showed that much of the observed difference was related to inter-reader differences rather than reader-computerized scheme differences. Computerized estimates were correlated with each of the radiologist's estimates (Spearman, p < 0.0001).

CONCLUSIONS

While variability between readers-based estimates of fissure integrity was smaller than differences between the computerized scheme and each of the readers, the result reported here are quite encouraging in that the magnitude of these differences were in the same magnitude, demonstrating the feasibility of using a computerized scheme for this purpose.

Boundary detection in medical images using edge following algorithm based on intensity gradient and texture gradient features

Finding the correct boundary in noisy images is still a difficult task. This paper introduces a new edge following technique for boundary detection in noisy images. Utilization of the proposed technique is exhibited via its application to various types of medical images. Our proposed technique can detect the boundaries of objects in noisy images using the information from the intensity gradient via the vector image model and the texture gradient via the edge map. The performance and robustness of the technique have been tested to segment objects in synthetic noisy images and medical images including prostates in ultrasound images, left ventricles in cardiac magnetic resonance (MR) images, aortas in cardiovascular MR images, and knee joints in computerized tomography images. We compare the proposed segmentation technique with the active contour models (ACM), geodesic active contour models, active contours without edges, gradient vector flow snake models, and ACMs based on vector field convolution, by using the skilled doctors' opinions as the ground truths. The results show that our technique performs very well and yields better performance than the classical contour models. The proposed method is robust and applicable on various kinds of noisy images without prior knowledge of noise properties.

Automatic segmentation of pulmonary lobes robust against incomplete fissures

A method for automatic segmentation of pulmonary lobes from computed tomography (CT) scans is presented that is robust against incomplete fissures. The method is based on a multiatlas approach in which existing lobar segmentations are deformed to test scans in which the fissures, the lungs, and the bronchial tree have been automatically segmented. The key element of our method is a cost function that exploits information from fissures, lung borders, and bronchial tree in an effective way, such that less reliable information (lungs, airways) is only used when the most reliable information (fissures) is missing. To cope with the anatomical variation in lobe shape, an atlas selection mechanism is introduced. The method is evaluated on two test sets of 120 scans in total. The results show that the lobe segmentation closely follows the fissures when they are present. In a simulated experiment in which parts of complete fissures are removed, the robustness of the method against different levels of incomplete fissures is shown. When the fissures are incomplete, an observer study shows agreement of the automatically determined lobe borders with a radiologist for 81% of the lobe borders on average.

Pulmonary lobe segmentation in CT examinations using implicit surface fitting

Lobe identification in computed tomography (CT) examinations is often an important consideration during the diagnostic process as well as during treatment planning because of their relative independence of each other in terms of anatomy and function. In this paper, we present a new automated scheme for segmenting lung lobes depicted on 3-D CT examinations. The unique characteristic of this scheme is the representation of fissures in the form of implicit functions using Radial Basis Functions (RBFs), capable of seamlessly interpolating "holes" in the detected fissures and smoothly extrapolating the fissure surfaces to the lung boundaries resulting in a "natural" segmentation of lung lobes. A previously developed statistically based approach is used to detect pulmonary fissures and the constraint points for implicit surface fitting are selected from detected fissure surfaces in a greedy manner to improve fitting efficiency. In a preliminary assessment study, lobe segmentation results of 65 chest CT examinations, five of which were reconstructed with three section thicknesses of 0.625 mm, 1.25 mm, and 2.5 mm, were subjectively and independently evaluated by two experienced chest radiologists using a five category rating scale (i.e., excellent, good, fair, poor, and unacceptable). Thirty-three of 65 examinations (50.8%) with a section thickness of 0.625 mm were rated as either "excellent" or "good" by both radiologists and only one case (1.5%) was rated by both radiologists as "poor" or "unacceptable." Comparable performance was obtained with a slice thickness of 1.25 mm, but substantial performance deterioration occurred in examinations with a section thickness of 2.5 mm. The advantages of this scheme are its full automation, relative insensitivity to fissure completeness, and ease of implementation.