A super-voxel-based method for generating surrogate lung ventilation images from CT

Purpose: This study aimed to develop and evaluate CTVISVD , a super-voxel-based method for surrogate computed tomography ventilation imaging (CTVI). Methods and Materials: The study used four-dimensional CT (4DCT) and single-photon emission computed tomography (SPECT) images and corresponding lung masks from 21 patients with lung cancer obtained from the Ventilation And Medical Pulmonary Image Registration Evaluation dataset. The lung volume of the exhale CT for each patient was segmented into hundreds of super-voxels using the Simple Linear Iterative Clustering (SLIC) method. These super-voxel segments were applied to the CT and SPECT images to calculate the mean density values (D _mean) and mean ventilation values (Vent _mean), respectively. The final CT-derived ventilation images were generated by interpolation from the D _mean values to yield CTVISVD. For the performance evaluation, the voxel- and region-wise differences between CTVISVD and SPECT were compared using Spearman's correlation and the Dice similarity coefficient index. Additionally, images were generated using two deformable image registration (DIR)-based methods, CTVIHU and CTVIJac, and compared with the SPECT images. Results: The correlation between the D _mean and Vent _mean of the super-voxel was 0.59 ± 0.09, representing a moderate-to-high correlation at the super-voxel level. In the voxel-wise evaluation, the CTVISVD method achieved a stronger average correlation (0.62 ± 0.10) with SPECT, which was significantly better than the correlations achieved with the CTVIHU (0.33 ± 0.14, p < 0.05) and CTVIJac (0.23 ± 0.11, p < 0.05) methods. For the region-wise evaluation, the Dice similarity coefficient of the high functional region for CTVISVD (0.63 ± 0.07) was significantly higher than the corresponding values for the CTVIHU (0.43 ± 0.08, p < 0.05) and CTVIJac (0.42 ± 0.05, p < 0.05) methods. Conclusion: The strong correlation between CTVISVD and SPECT demonstrates the potential usefulness of this novel method of ventilation estimation for surrogate ventilation imaging.

A Fissure-Aided Registration Approach for Automatic Pulmonary Lobe Segmentation Using Deep Learning

The segmentation of pulmonary lobes is important in clinical assessment, lesion location, and surgical planning. Automatic lobe segmentation is challenging, mainly due to the incomplete fissures or the morphological variation resulting from lung disease. In this work, we propose a learning-based approach that incorporates information from the local fissures, the whole lung, and priori pulmonary anatomy knowledge to separate the lobes robustly and accurately. The prior pulmonary atlas is registered to the test CT images with the aid of the detected fissures. The result of the lobe segmentation is obtained by mapping the deformation function on the lobes-annotated atlas. The proposed method is evaluated in a custom dataset with COPD. Twenty-four CT scans randomly selected from the custom dataset were segmented manually and are available to the public. The experiments showed that the average dice coefficients were 0.95, 0.90, 0.97, 0.97, and 0.97, respectively, for the right upper, right middle, right lower, left upper, and left lower lobes. Moreover, the comparison of the performance with a former learning-based segmentation approach suggests that the presented method could achieve comparable segmentation accuracy and behave more robustly in cases with morphological specificity.

Computer-assisted image-based risk analysis and planning in lung surgery - a review

In this paper, we give an overview on current trends in computer-assisted image-based methods for risk analysis and planning in lung surgery and present our own developments with a focus on computed tomography (CT) based algorithms and applications. The methods combine heuristic, knowledge based image processing algorithms for segmentation, quantification and visualization based on CT images of the lung. Impact for lung surgery is discussed regarding risk assessment, quantitative assessment of resection strategies, and surgical guiding. In perspective, we discuss the role of deep-learning based AI methods for further improvements.

Performance of virtual non-contrast images generated on clinical photon-counting detector CT for emphysema quantification: proof of concept

OBJECTIVE

To evaluate the performance of virtual non-contrast images (VNC) compared to true non-contrast (TNC) images in photon-counting detector computed tomography (PCD-CT) for the evaluation of lung parenchyma and emphysema quantification.

METHODS

65 (mean age 73 years; 48 male) consecutive patients who underwent a three-phase (non-contrast, arterial and venous) chest/abdomen CT on a first-generation dual-source PCD-CT were retrospectively included. Scans were performed in the multienergy (QuantumPlus) mode at 120 kV with 70 ml intravenous contrast agent at an injection rate of 4 ml s-1. VNC were reconstructed from the arterial (VNCart) and venous phase (VNCven). TNC and VNC images of the lung were assessed quantitatively by calculating the global noise index (GNI) and qualitatively by two independent, blinded readers (overall image quality and emphysema assessment). Emphysema quantification was performed using a commercially available software tool at a threshold of -950 HU for all data sets. TNC images served as reference standard for emphysema quantification. Low attenuation values (LAV) were compared in a Bland-Altman plot.

RESULTS

GNI was similar in VNCart (103.0 ± 30.1) and VNCven (98.2 ± 22.2) as compared to TNC (100.9 ± 19.0, p = 0.546 and p = 0.272, respectively). Subjective image quality (emphysema assessment and overall image quality) was highest for TNC (p = 0.001), followed by VNCven and VNCart. Both, VNCart and VNCven showed no significant difference in emphysema quantification as compared to TNC (p = 0.409 vs. p = 0.093; respectively).

CONCLUSION

Emphysema evaluation is feasible using virtual non-contrast images from PCD-CT.

ADVANCES IN KNOWLEDGE

Emphysema quantification is feasible and accurate using VNC images in PCD-CT. Based on these findings, additional TNC scans for emphysema quantification could be omitted in the future.

A fully automated noncontrast CT 3-D reconstruction algorithm enabled accurate anatomical demonstration for lung segmentectomy

Background

Three‐dimensional reconstruction of chest computerized tomography (CT) excels in intuitively demonstrating anatomical patterns for pulmonary segmentectomy. However, current methods are labor‐intensive and rely on contrast CT. We hereby present a novel fully automated reconstruction algorithm based on noncontrast CT and assess its performance both independently and in combination with surgeons.

Methods

A retrospective pilot study was performed. Patients between May 2020 to August 2020 who underwent segmentectomy in our single institution were enrolled. Noncontrast CTs were used for reconstruction. In the first part of the study, the accuracy of the demonstration of anatomical variants by either automated or manual reconstruction algorithm were compared to surgical observation, respectively. In the second part of the study, we tested the accuracy of the identification of anatomical variants by four independent attendees who reviewed 3‐D reconstruction in combination with CT scans.

Results

A total of 20 cases were enrolled in this study. All segments were represented in this study with two left S1‐3, two left S4 + 5, one left S6, five left basal segmentectomies, one right S1, three right S2, 1 right S2b + 3a, one right S3, two right S6 and two right basal segmentectomies. The median time consumption for the automated reconstruction was 280 (205–324) s. Accurate vessel and bronchial detection were achieved in 85% by the AI approach and 80% by Mimics, p = 1.00. The accuracy of vessel classification was 80 and 95% by AI and manual approaches, respectively, p = 0.34. In real‐world application, the accuracy of the identification of anatomical variant by thoracic surgeons was 85% by AI+CT, and the median time consumption was 2 (1–3) min.

Conclusions

The AI reconstruction algorithm overcame defects of traditional methods and is valuable in surgical planning for segmentectomy. With the AI reconstruction, surgeons may achieve high identification accuracy of anatomical patterns in a short time frame.

Demarcation of arteriopulmonary segments: a novel and effective method for the identification of pulmonary segments

Objective

Each pulmonary segment is an anatomical and functional unit. However, it is fundamentally difficult to precisely distinguish every pulmonary segment using the conventional pulmonary intersegmental planes from computed tomography images. Building arteriopulmonary segments is likely to be an effective way to identify pulmonary segments.

Methods

The thoracic computed tomography images of 40 patients were collected. The anatomic structures of interest were extracted in the transverse, sagittal, and coronal planes using the semi-automated segmentation tools provided by Amira software. The intrapulmonary vessels were subsequently segmented and reconstructed. The distributions of the pulmonary arteries, veins, and bronchi were observed. In patients with pulmonary masses, the mass was also reconstructed.

Results

The three-dimensional reconstructed images showed the branches of the pulmonary artery ramified up to their eighth order covering the entire lung as well as evident intersegmental gaps without pulmonary arteries. The segmental artery was closely accompanied by the segmental bronchi in 486 pulmonary segments (90% of total number of segments). The size and spatial location of the pulmonary mass within a pulmonary segment were also clearly visible.

Conclusions

Demarcation of arteriopulmonary segments can be used to precisely distinguish every pulmonary segment and provide its detailed anatomical structure before pulmonary segmentectomy.

CT image segmentation for inflamed and fibrotic lungs using a multi-resolution convolutional neural network

The purpose of this study was to develop a fully-automated segmentation algorithm, robust to various density enhancing lung abnormalities, to facilitate rapid quantitative analysis of computed tomography images. A polymorphic training approach is proposed, in which both specifically labeled left and right lungs of humans with COPD, and nonspecifically labeled lungs of animals with acute lung injury, were incorporated into training a single neural network. The resulting network is intended for predicting left and right lung regions in humans with or without diffuse opacification and consolidation. Performance of the proposed lung segmentation algorithm was extensively evaluated on CT scans of subjects with COPD, confirmed COVID-19, lung cancer, and IPF, despite no labeled training data of the latter three diseases. Lobar segmentations were obtained using the left and right lung segmentation as input to the LobeNet algorithm. Regional lobar analysis was performed using hierarchical clustering to identify radiographic subtypes of COVID-19. The proposed lung segmentation algorithm was quantitatively evaluated using semi-automated and manually-corrected segmentations in 87 COVID-19 CT images, achieving an average symmetric surface distance of [Formula: see text] mm and Dice coefficient of [Formula: see text]. Hierarchical clustering identified four radiographical phenotypes of COVID-19 based on lobar fractions of consolidated and poorly aerated tissue. Lower left and lower right lobes were consistently more afflicted with poor aeration and consolidation. However, the most severe cases demonstrated involvement of all lobes. The polymorphic training approach was able to accurately segment COVID-19 cases with diffuse consolidation without requiring COVID-19 cases for training.

Quantitative CT Analysis in Patients with Pulmonary Emphysema: Do Calculated Differences Between Full Inspiration and Expiration Correlate with Lung Function?

Purpose

The aim of this retrospective study was to evaluate correlations between parameters of quantitative computed tomography (QCT) analysis, especially the 15th percentile of lung attenuation (P15), and parameters of clinical tests in a large group of patients with pulmonary emphysema.

Patients and Methods

One hundred and seventy-two patients with pulmonary emphysema and chronic obstructive pulmonary disease (COPD) global initiative for chronic obstructive lung disease (GOLD) stage 3 or 4 were assessed by nonenhanced thin-section CT scans in full inspiratory and expiratory breath-hold, pulmonary function test (PFT), a 6-minute walk test (6MWT), and quality of life questionnaires (SGRQ and CAT). QCT parameters included total lung volume (TLV), total emphysema score (TES), and P15, all measured at inspiration (IN) and expiration (EX). Differences between inspiration and expiration were calculated for TLV (TLV_Diff), TES (TES_Diff), and P15 (P15_Diff). Spearman correlation analysis was performed.

Results

CT-measured lung volume in inspiration (TLV_IN) correlated strongly with spirometry-measured total lung capacity (TLC) (r=0.81, p<0.001) and moderately to strongly with residual volume (RV), forced vital capacity (FVC), and forced expiratory volume in 1 second (FEV₁)/FVC (r=0.60, 0.56, and −0.49, each p<0.001). Lung volume in expiration (TLV_EX) correlated moderately to strongly with TLC, RV and FEV₁/FVC ratio (r=0.75, 0.66, and −0.43, each p<0.001). TES and P15 showed stronger correlations with the carbon monoxide transfer coefficient (KCO%) (r= −0.42, 0.44, both p<0.001), when measured during expiration. P15_Diff correlated moderately with KCO% and carbon monoxide diffusing capacity (DLCO%) (r= 0.41, 0.40, both p<0.001). The 6MWT and most QCT parameters showed significant differences between COPD GOLD 3 and 4 groups.

Conclusion

Our results suggest that QCT can help predict the severity of lung function decrease in patients with pulmonary emphysema and COPD GOLD 3 or 4. Some QCT parameters, including P15_EX and P15_Diff, correlated moderately to strongly with parameters of pulmonary function tests.

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.

Quantitative CT analysis in patients with pulmonary emphysema: is lung function influenced by concomitant unspecific pulmonary fibrosis?

Purpose

Quantitative analysis of CT scans has proven to be a reproducible technique, which might help to understand the pathophysiology of chronic obstructive pulmonary disease (COPD) and combined pulmonary fibrosis and emphysema. The aim of this retrospective study was to find out if the lung function of patients with COPD with Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages III or IV and pulmonary emphysema is measurably influenced by high attenuation areas as a correlate of concomitant unspecific fibrotic changes of lung parenchyma.

Patients and methods

Eighty-eight patients with COPD GOLD stage III or IV underwent CT and pulmonary function tests. Quantitative CT analysis was performed to determine low attenuation volume (LAV) and high attenuation volume (HAV), which are considered to be equivalents of fibrotic (HAV) and emphysematous (LAV) changes of lung parenchyma. Both parameters were determined for the whole lung, as well as peripheral and central lung areas only. Multivariate regression analysis was used to correlate HAV with different parameters of lung function.

Results

Unlike LAV, HAV did not show significant correlation with parameters of lung function. Even in patients with a relatively high HAV of more than 10%, in contrast to HAV (p=0.786) only LAV showed a significantly negative correlation with forced expiratory volume in 1 second (r=−0.309, R²=0.096, p=0.003). A severe decrease of DLCO% was associated with both larger HAV (p=0.045) and larger LAV (p=0.001). Residual volume and FVC were not influenced by LAV or HAV.

Conclusion

In patients with COPD GOLD stage III-IV, emphysematous changes of lung parenchyma seem to have such a strong influence on lung function, which is a possible effect of concomitant unspecific fibrosis is overwhelmed.

Effective and Reliable Framework for Lung Nodules Detection from CT Scan Images

Lung cancer is considered more serious among other prevailing cancer types. One of the reasons for it is that it is usually not diagnosed until it has spread and by that time it becomes very difficult to treat. Early detection of lung cancer can significantly increase the chances of survival of a cancer patient. An effective nodule detection system can play a key role in early detection of lung cancer thus increasing the chances of successful treatment. In this research work, we have proposed a novel classification framework for nodule classification. The framework consists of multiple phases that include image contrast enhancement, segmentation, optimal feature extraction, followed by employment of these features for training and testing of Support Vector Machine. We have empirically tested the efficacy of our technique by utilizing the well-known Lung Image Consortium Database (LIDC) dataset. The empirical results suggest that the technique is highly effective for reducing the false positive rates. We were able to receive an impressive sensitivity rate of 97.45%.

FissureNet: A Deep Learning Approach For Pulmonary Fissure Detection in CT Images

Pulmonary fissure detection in computed tomography (CT) is a critical component for automatic lobar segmentation. The majority of fissure detection methods use feature descriptors that are hand-crafted, low-level, and have local spatial extent. The design of such feature detectors is typically targeted toward normal fissure anatomy, yielding low sensitivity to weak, and abnormal fissures that are common in clinical data sets. Furthermore, local features commonly suffer from low specificity, as the complex textures in the lung can be indistinguishable from the fissure when the global context is not considered. We propose a supervised discriminative learning framework for simultaneous feature extraction and classification. The proposed framework, called FissureNet, is a coarse-to-fine cascade of two convolutional neural networks. The coarse-to-fine strategy alleviates the challenges associated with training a network to segment a thin structure that represents a small fraction of the image voxels. FissureNet was evaluated on a cohort of 3706 subjects with inspiration and expiration 3DCT scans from the COPDGene clinical trial and a cohort of 20 subjects with 4DCT scans from a lung cancer clinical trial. On both data sets, FissureNet showed superior performance compared with a deep learning approach using the U-Net architecture and a Hessian-based fissure detection method in terms of area under the precision-recall curve (PR-AUC). The overall PR-AUC for FissureNet, U-Net, and Hessian on the COPDGene (lung cancer) data set was 0.980 (0.966), 0.963 (0.937), and 0.158 (0.182), respectively. On a subset of 30 COPDGene scans, FissureNet was compared with a recently proposed advanced fissure detection method called derivative of sticks (DoS) and showed superior performance with a PR-AUC of 0.991 compared with 0.668 for DoS.

Semi-automatic Methods for Airway and Adjacent Vessel Measurement in Bronchiectasis Patterns in Lung HRCT Images of Cystic Fibrosis Patients

Airway and vessel characterization of bronchiectasis patterns in lung high-resolution computed tomography (HRCT) images of cystic fibrosis (CF) patients is very important to compute the score of disease severity. We propose a hybrid and evolutionary optimized threshold and model-based method for characterization of airway and vessel in lung HRCT images of CF patients. First, the initial model of airway and vessel is obtained using the enhanced threshold-based method. Then, the model is fitted to the actual image by optimizing its parameters using particle swarm optimization (PSO) evolutionary algorithm. The experimental results demonstrated the outperformance of the proposed method over its counterpart in R-squared, mean and variance of error, and run time. Moreover, the proposed method outperformed its counterpart for airway inner diameter/vessel diameter (AID/VD) and airway wall thickness/vessel diameter (AWT/VD) biomarkers in R-squared and slope of regression analysis.

Quantitative computed tomography applied to interstitial lung diseases

OBJECTIVES

To evaluate a new image marker that retrieves information from computed tomography (CT) density histograms, with respect to classification properties between different lung parenchyma groups. Furthermore, to conduct a comparison of the new image marker with conventional markers.

MATERIALS AND METHODS

Density histograms from 220 different subjects (normal = 71; emphysema = 73; fibrotic = 76) were used to compare the conventionally applied emphysema index (EI), 15^th percentile value (PV), mean value (MV), variance (V), skewness (S), kurtosis (K), with a new histogram's functional shape (HFS) method. Multinomial logistic regression (MLR) analyses was performed to calculate predictions of different lung parenchyma group membership using the individual methods, as well as combinations thereof, as covariates. Overall correct assigned subjects (OCA), sensitivity (sens), specificity (spec), and Nagelkerke's pseudo R² (NR²) effect size were estimated. NR² was used to set up a ranking list of the different methods.

RESULTS

MLR indicates the highest classification power (OCA of 92%; sens 0.95; spec 0.89; NR² 0.95) when all histogram analyses methods were applied together in the MLR. Highest classification power among individually applied methods was found using the HFS concept (OCA 86%; sens 0.93; spec 0.79; NR² 0.80). Conventional methods achieved lower classification potential on their own: EI (OCA 69%; sens 0.95; spec 0.26; NR² 0.52); PV (OCA 69%; sens 0.90; spec 0.37; NR² 0.57); MV (OCA 65%; sens 0.71; spec 0.58; NR² 0.61); V (OCA 66%; sens 0.72; spec 0.53; NR² 0.66); S (OCA 65%; sens 0.88; spec 0.26; NR² 0.55); and K (OCA 63%; sens 0.90; spec 0.16; NR² 0.48).

CONCLUSION

The HFS method, which was so far applied to a CT bone density curve analysis, is also a remarkable information extraction tool for lung density histograms. Presumably, being a principle mathematical approach, the HFS method can extract valuable health related information also from histograms from complete different areas.

Quantitative computed tomography detects interstitial lung diseases proven by biopsy

Background: The Quantitative chest CT (QCT) is emerging as a promising tool in the assessment of interstitial lung disease (ILD). However, the precise relationship between QCT parameters and the fibrosis detectable in lung tissue, remains to be established. Objectives: The aim of this study was to compare QCT and histopathological features in patients with ILD. Moreover we verified if the QCT assessment is similar in patients with or without a ILD diagnosis proven by a biopsy. Methods: Twenty patients affected by ILD who underwent a chest CT and, later, a lung biopsy, were enrolled. Patients were divided according to the histopathological findings (IPF vs sarcoidosis) in two groups (respectively bIPF and bSarc). Other 20 patients with a radiological diagnosis of IPF were included in a control group (rIPF). All CTs were post-processed with a free software (Horos) in order to obtain an ILD quantitative assessment. Results: There were no differences in terms of gender, smoking habit and spirometric values between patients' groups. rIPF subjects were older than the other: 70 vs 59 and 47 years (p<0.001). A different distribution of QCT parameters was observed between bIPF and bSarc (p<0.01) while it was comparable within bIPF and rIPF. Conclusions: QCT parameters were similar in subjects affected by the same type of ILD detected with biopsy and with CT alone. These findings make stronger the assumption that QCT can identify the presence of pulmonary fibrosis and, ultimately, that it can represent an useful and effective tool to assess ILD. (Sarcoidosis Vasc Diffuse Lung Dis 2018; 35: 16-20).

Lung densitometry: why, how and when

Lung densitometry assesses with computed tomography (CT) the X-ray attenuation of the pulmonary tissue which reflects both the degree of inflation and the structural lung abnormalities implying decreased attenuation, as in emphysema and cystic diseases, or increased attenuation, as in fibrosis. Five reasons justify replacement with lung densitometry of semi-quantitative visual scales used to measure extent and severity of diffuse lung diseases: (I) improved reproducibility; (II) complete vs. discrete assessment of the lung tissue; (III) shorter computation times; (IV) better correlation with pathology quantification of pulmonary emphysema; (V) better or equal correlation with pulmonary function tests (PFT). Commercially and open platform software are available for lung densitometry. It requires attention to technical and methodological issues including CT scanner calibration, radiation dose, and selection of thickness and filter to be applied to sections reconstructed from whole-lung CT acquisition. Critical is also the lung volume reached by the subject at scanning that can be measured in post-processing and represent valuable information per se. The measurements of lung density include mean and standard deviation, relative area (RA) at -970, -960 or -950 Hounsfield units (HU) and 1st and 15th percentile for emphysema in inspiratory scans, and RA at -856 HU for air trapping in expiratory scans. Kurtosis and skewness are used for evaluating pulmonary fibrosis in inspiratory scans. The main indication for lung densitometry is assessment of emphysema component in the single patient with chronic obstructive pulmonary diseases (COPD). Additional emerging applications include the evaluation of air trapping in COPD patients and in subjects at risk of emphysema and the staging in patients with lymphangioleiomyomatosis (LAM) and with pulmonary fibrosis. It has also been applied to assess prevalence of smoking-related emphysema and to monitor progression of smoking-related emphysema, alpha1 antitrypsin deficiency emphysema, and pulmonary fibrosis. Finally, it is recommended as end-point in pharmacological trials of emphysema and lung fibrosis.

Fast interactive segmentation of the pulmonary lobes from thoracic computed tomography data

Automated lung lobe segmentation methods often fail for challenging and clinically relevant cases with incomplete fissures or substantial amounts of pathology. We present a fast and intuitive method to interactively correct a given lung lobe segmentation or to quickly create a lobe segmentation from scratch based on a lung mask. A given lobar boundary is converted into a mesh by principal component analysis of 3D lobar boundary markers to obtain a plane where nodes correspond to the position of the markers. An observer can modify the mesh by drawing on 2D slices in arbitrary orientations. After each drawing, the mesh is immediately adapted in a 3D region around the user interaction. For evaluation we participated in the international lung lobe segmentation challenge LObe and lung analysis 2011 (LOLA11). Two observers applied the method to correct a given lung lobe segmentation obtained by a fully automatic method for all 55 CT scans of LOLA11. On average observer 1/2 required 8  ±  4/25  ±  12 interactions per case and took 1:30  ±  0:34/3:19  ±  1:29 min. The average distances to the reference segmentation were improved from an initial 2.68  ±  14.71 mm to 0.89  ±  1.63/0.74  ±  1.51 mm. In addition, one observer applied the proposed method to create a segmentation from scratch. This took 3:44  ±  0:58 minutes on average per case, applying an average of 20  ±  3 interactions to reach an average distance to the reference of 0.77  ±  1.14 mm. Thus, both the interactive corrections and the creation of a segmentation from scratch were feasible in a short time with excellent results and only little interaction. Since the mesh adaptation is independent of image features, the method can successfully handle patients with severe pathologies, provided that the human operator is capable of correctly indicating the lobar boundaries.

Comparison of distinctive models for calculating an interlobar emphysema heterogeneity index in patients prior to endoscopic lung volume reduction

Background

The degree of interlobar emphysema heterogeneity is thought to play an important role in the outcome of endoscopic lung volume reduction (ELVR) therapy of patients with advanced COPD. There are multiple ways one could possibly define interlobar emphysema heterogeneity, and there is no standardized definition.

Purpose

The aim of this study was to derive a formula for calculating an interlobar emphysema heterogeneity index (HI) when evaluating a patient for ELVR. Furthermore, an attempt was made to identify a threshold for relevant interlobar emphysema heterogeneity with regard to ELVR.

Patients and methods

We retrospectively analyzed 50 patients who had undergone technically successful ELVR with placement of one-way valves at our institution and had received lung function tests and computed tomography scans before and after treatment. Predictive accuracy of the different methods for HI calculation was assessed with receiver-operating characteristic curve analysis, assuming a minimum difference in forced expiratory volume in 1 second of 100 mL to indicate a clinically important change.

Results

The HI defined as emphysema score of the targeted lobe (TL) minus emphysema score of the ipsilateral nontargeted lobe disregarding the middle lobe yielded the best predicative accuracy (AUC =0.73, P=0.008). The HI defined as emphysema score of the TL minus emphysema score of the lung without the TL showed a similarly good predictive accuracy (AUC =0.72, P=0.009). Subgroup analysis suggests that the impact of interlobar emphysema heterogeneity is of greater importance in patients with upper lobe predominant emphysema than in patients with lower lobe predominant emphysema.

Conclusion

This study reveals the most appropriate ways of calculating an interlobar emphysema heterogeneity with regard to ELVR.

Thoracic CT-MRI coregistration for regional pulmonary structure-function measurements of obstructive lung disease

PURPOSE

Recent pulmonary imaging research has revealed that in patients with chronic obstructive pulmonary disease (COPD) and asthma, structural and functional abnormalities are spatially heterogeneous. This novel information may help optimize treatment in individual patients, monitor interventional efficacy, and develop new treatments. Moreover, by automating the measurement of regional biomarkers for the 19 different anatomical lung segments, there is an opportunity to embed imaging biomarkers into clinically acceptable clinical workflows and improve lung disease clinical care. Therefore, to exploit the regional structure-function information provided by thoracic imaging, and as a first step toward this goal, our objective was to develop a fully automated registration pipeline for thoracic x-ray computed tomography (CT) and inhaled gas functional magnetic resonance imaging (MRI) whole lung and segmental structure-function biomarkers.

METHODS

Thirty-five patients including 15 severe, poorly controlled asthmatics and 20 COPD patients [classified according to the global initiative for chronic obstructive lung disease (GOLD) criteria)] provided written informed consent to a study protocol approved by Health Canada and underwent pulmonary function tests, MRI, and CT during a single 2-hour visit. Using this diverse patient dataset, we developed and evaluated a joint deformable registration approach to simultaneously coregister CT with both ¹ H and ³ He MRI by enforcing the similarity of the deformation fields from the two individual registrations. We derived a simpler model that was equivalent to the original challenging optimization problem through variational analysis and the simpler model gave rise to an efficient numerical solver that was parallelized on a graphics processing unit. The coregistered CT-³ He MRI and whole lung/segmental lung masks were used to generate whole lung and segmental ³ He MRI ventilation defect percent (VDP). To estimate fiducial localization reproducibility, a single observer manually identified 109 pairs of CT and ³ He MRI fiducials for 35 patient images on five separate occasions and determined the fiducial localization error (FLE). CT-³ He MRI registration accuracy was evaluated using the target registration error (TRE). Whole lung VDP generated using the algorithm was compared with VDP generated using a previously validated semiautomated approach and computational efficiency was evaluated using run time.

RESULTS

In 35 patients including 15 with severe asthma and 20 with COPD, mean forced expiratory volume in 1 s (FEV₁ ) was 63±24%_pred and FEV₁ /forced vital capacity (FVC) was 54 ± 17%. FLE was 0.16 mm and 0.34 mm for ³ He MRI and CT, respectively. TRE was 4.5 ± 2.0 mm, 4.0 ± 1.7 mm, 4.8 ± 2.3 mm for asthma, COPD GOLD II, and GOLD III groups, respectively, with a mean of 4.4 ± 2.0 mm for the entire dataset. TRE was significantly improved for joint CT-¹ H/³ He MRI registration compared with CT-¹ H MRI rigid registration (P < 0.0001). Whole lung VDP generated using the pipeline was not significantly different (P = 0.37) compared to a semiautomated method with which it was strongly correlated (r = 0.93, P < 0.0001). The fully automated pipeline required 11 ± 0.4 min to generate whole lung and segmental VDP.

CONCLUSIONS

For a diverse group of patients with COPD and asthma, whole lung and segmental VDP was measured using an automated lung image analysis pipeline which provides a way to incorporate lung functional biomarkers into clinical research and patient care.

Chronic Obstructive Pulmonary Disease: Lobar Analysis with Hyperpolarized 129Xe MR Imaging

Purpose To compare lobar ventilation and apparent diffusion coefficient (ADC) values obtained with hyperpolarized xenon 129 (129Xe) magnetic resonance (MR) imaging to quantitative computed tomography (CT) metrics on a lobar basis and pulmonary function test (PFT) results on a whole-lung basis in patients with chronic obstructive pulmonary disease (COPD). Materials and Methods The study was approved by the National Research Ethics Service Committee; written informed consent was obtained from all patients. Twenty-two patients with COPD (Global Initiative for Chronic Obstructive Lung Disease stage II-IV) underwent hyperpolarized 129Xe MR imaging at 1.5 T, quantitative CT, and PFTs. Whole-lung and lobar 129Xe MR imaging parameters were obtained by using automated segmentation of multisection hyperpolarized 129Xe MR ventilation images and hyperpolarized 129Xe MR diffusion-weighted images after coregistration to CT scans. Whole-lung and lobar quantitative CT-derived metrics for emphysema and bronchial wall thickness were calculated. Pearson correlation coefficients were used to evaluate the relationship between imaging measures and PFT results. Results Percentage ventilated volume and average ADC at lobar 129Xe MR imaging showed correlation with percentage emphysema at lobar quantitative CT (r = -0.32, P < .001 and r = 0.75, P < .0001, respectively). The average ADC at whole-lung 129Xe MR imaging showed moderate correlation with PFT results (percentage predicted transfer factor of the lung for carbon monoxide [Tlco]: r = -0.61, P < .005) and percentage predicted functional residual capacity (r = 0.47, P < .05). Whole-lung quantitative CT percentage emphysema also showed statistically significant correlation with percentage predicted Tlco (r = -0.65, P < .005). Conclusion Lobar ventilation and ADC values obtained from hyperpolarized 129Xe MR imaging demonstrated correlation with quantitative CT percentage emphysema on a lobar basis and with PFT results on a whole-lung basis. © RSNA, 2016.

Development and validation of a prediction model for measurement variability of lung nodule volumetry in patients with pulmonary metastases

OBJECTIVES

To develop a prediction model for the variability range of lung nodule volumetry and validate the model in detecting nodule growth.

MATERIALS AND METHODS

For model development, 50 patients with metastatic nodules were prospectively included. Two consecutive CT scans were performed to assess volumetry for 1,586 nodules. Nodule volume, surface voxel proportion (SVP), attachment proportion (AP) and absolute percentage error (APE) were calculated for each nodule and quantile regression analyses were performed to model the 95% percentile of APE. For validation, 41 patients who underwent metastasectomy were included. After volumetry of resected nodules, sensitivity and specificity for diagnosis of metastatic nodules were compared between two different thresholds of nodule growth determination: uniform 25% volume change threshold and individualized threshold calculated from the model (estimated 95% percentile APE).

RESULTS

SVP and AP were included in the final model: Estimated 95% percentile APE = 37.82 · SVP + 48.60 · AP-10.87. In the validation session, the individualized threshold showed significantly higher sensitivity for diagnosis of metastatic nodules than the uniform 25% threshold (75.0% vs. 66.0%, P = 0.004) CONCLUSION: Estimated 95% percentile APE as an individualized threshold of nodule growth showed greater sensitivity in diagnosing metastatic nodules than a global 25% threshold.

KEY POINTS

• The 95 % percentile APE of a particular nodule can be predicted. • Estimated 95 % percentile APE can be utilized as an individualized threshold. • More sensitive diagnosis of metastasis can be made with an individualized threshold. • Tailored nodule management can be provided during nodule growth follow-up.

An approach for reducing the error rate in automated lung segmentation

Robust lung segmentation is challenging, especially when tens of thousands of lung CT scans need to be processed, as required by large multi-center studies. The goal of this work was to develop and assess a method for the fusion of segmentation results from two different methods to generate lung segmentations that have a lower failure rate than individual input segmentations. As basis for the fusion approach, lung segmentations generated with a region growing and model-based approach were utilized. The fusion result was generated by comparing input segmentations and selectively combining them using a trained classification system. The method was evaluated on a diverse set of 204 CT scans of normal and diseased lungs. The fusion approach resulted in a Dice coefficient of 0.9855±0.0106 and showed a statistically significant improvement compared to both input segmentation methods. In addition, the failure rate at different segmentation accuracy levels was assessed. For example, when requiring that lung segmentations must have a Dice coefficient of better than 0.97, the fusion approach had a failure rate of 6.13%. In contrast, the failure rate for region growing and model-based methods was 18.14% and 15.69%, respectively. Therefore, the proposed method improves the quality of the lung segmentations, which is important for subsequent quantitative analysis of lungs. Also, to enable a comparison with other methods, results on the LOLA11 challenge test set are reported.

Development of a minipig model for lung injury induced by a single high-dose radiation exposure and evaluation with thoracic computed tomography

Radiation-induced lung injury (RILI) due to nuclear or radiological exposure remains difficult to treat because of insufficient clinical data. The goal of this study was to establish an appropriate and efficient minipig model and introduce a thoracic computed tomography (CT)-based method to measure the progression of RILI. Göttingen minipigs were allocated to control and irradiation groups. The most obvious changes in the CT images after irradiation were peribronchial opacification, interlobular septal thickening, and lung volume loss. Hounsfield units (HU) in the irradiation group reached a maximum level at 6 weeks and decreased thereafter, but remained higher than those of the control group. Both lung area and cardiac right lateral shift showed significant changes at 22 weeks post irradiation. The white blood cell (WBC) count, a marker of pneumonitis, increased and reached a maximum at 6 weeks in both peripheral blood and bronchial alveolar lavage fluid. Microscopic findings at 22 weeks post irradiation were characterized by widening of the interlobular septum, with dense fibrosis and an increase in the radiation dose-dependent fibrotic score. Our results also showed that WBC counts and microscopic findings were positively correlated with the three CT parameters. In conclusion, the minipig model can provide useful clinical data regarding RILI caused by the adverse effects of high-dose radiotherapy. Peribronchial opacification, interlobular septal thickening, and lung volume loss are three quantifiable CT parameters that can be used as a simple method for monitoring the progression of RILI.

Fully Automated Pulmonary Lobar Segmentation: Influence of Different Prototype Software Programs onto Quantitative Evaluation of Chronic Obstructive Lung Disease

Objectives

Surgical or bronchoscopic lung volume reduction (BLVR) techniques can be beneficial for heterogeneous emphysema. Post-processing software tools for lobar emphysema quantification are useful for patient and target lobe selection, treatment planning and post-interventional follow-up. We aimed to evaluate the inter-software variability of emphysema quantification using fully automated lobar segmentation prototypes.

Material and Methods

66 patients with moderate to severe COPD who underwent CT for planning of BLVR were included. Emphysema quantification was performed using 2 modified versions of in-house software (without and with prototype advanced lung vessel segmentation; programs 1 [YACTA v.2.3.0.2] and 2 [YACTA v.2.4.3.1]), as well as 1 commercial program 3 [Pulmo3D VA30A_HF2] and 1 pre-commercial prototype 4 [CT COPD ISP ver7.0]). The following parameters were computed for each segmented anatomical lung lobe and the whole lung: lobar volume (LV), mean lobar density (MLD), 15^th percentile of lobar density (15^th), emphysema volume (EV) and emphysema index (EI). Bland-Altman analysis (limits of agreement, LoA) and linear random effects models were used for comparison between the software.

Results

Segmentation using programs 1, 3 and 4 was unsuccessful in 1 (1%), 7 (10%) and 5 (7%) patients, respectively. Program 2 could analyze all datasets. The 53 patients with successful segmentation by all 4 programs were included for further analysis. For LV, program 1 and 4 showed the largest mean difference of 72 ml and the widest LoA of [-356, 499 ml] (p<0.05). Program 3 and 4 showed the largest mean difference of 4% and the widest LoA of [-7, 14%] for EI (p<0.001).

Conclusions

Only a single software program was able to successfully analyze all scheduled data-sets. Although mean bias of LV and EV were relatively low in lobar quantification, ranges of disagreement were substantial in both of them. For longitudinal emphysema monitoring, not only scanning protocol but also quantification software needs to be kept constant.

Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching

Dynamic and longitudinal lung CT imaging produce 4D lung image data sets, enabling applications like radiation treatment planning or assessment of response to treatment of lung diseases. In this paper, we present a 4D lung segmentation method that mutually utilizes all individual CT volumes to derive segmentations for each CT data set. Our approach is based on a 3D robust active shape model and extends it to fully utilize 4D lung image data sets. This yields an initial segmentation for the 4D volume, which is then refined by using a 4D optimal surface finding algorithm. The approach was evaluated on a diverse set of 152 CT scans of normal and diseased lungs, consisting of total lung capacity and functional residual capacity scan pairs. In addition, a comparison to a 3D segmentation method and a registration based 4D lung segmentation approach was performed. The proposed 4D method obtained an average Dice coefficient of 0.9773 ± 0.0254, which was statistically significantly better (p value ≪0.001) than the 3D method (0.9659 ± 0.0517). Compared to the registration based 4D method, our method obtained better or similar performance, but was 58.6% faster. Also, the method can be easily expanded to process 4D CT data sets consisting of several volumes.

A method for avoiding overlap of left and right lungs in shape model guided segmentation of lungs in CT volumes

PURPOSE

The automated correct segmentation of left and right lungs is a nontrivial problem, because the tissue layer between both lungs can be quite thin. In the case of lung segmentation with left and right lung models, overlapping segmentations can occur. In this paper, the authors address this issue and propose a solution for a model-based lung segmentation method.

METHODS

The thin tissue layer between left and right lungs is detected by means of a classification approach and utilized to selectively modify the cost function of the lung segmentation method. The approach was evaluated on a diverse set of 212 CT scans of normal and diseased lungs. Performance was assessed by utilizing an independent reference standard and by means of comparison to the standard segmentation method without overlap avoidance.

RESULTS

For cases where the standard approach produced overlapping segmentations, the proposed method significantly (p = 1.65 × 10(-9)) reduced the overlap by 97.13% on average (median: 99.96%). In addition, segmentation accuracy assessed with the Dice coefficient showed a statistically significant improvement (p = 7.5 × 10(-5)) and was 0.9845 ± 0.0111. For cases where the standard approach did not produce an overlap, performance of the proposed method was not found to be significantly different.

CONCLUSIONS

The proposed method improves the quality of the lung segmentations, which is important for subsequent quantitative analysis steps.

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.

CIDI-lung-seg: a single-click annotation tool for automatic delineation of lungs from CT scans

Accurate and fast extraction of lung volumes from computed tomography (CT) scans remains in a great demand in the clinical environment because the available methods fail to provide a generic solution due to wide anatomical variations of lungs and existence of pathologies. Manual annotation, current gold standard, is time consuming and often subject to human bias. On the other hand, current state-of-the-art fully automated lung segmentation methods fail to make their way into the clinical practice due to their inability to efficiently incorporate human input for handling misclassifications and praxis. This paper presents a lung annotation tool for CT images that is interactive, efficient, and robust. The proposed annotation tool produces an "as accurate as possible" initial annotation based on the fuzzy-connectedness image segmentation, followed by efficient manual fixation of the initial extraction if deemed necessary by the practitioner. To provide maximum flexibility to the users, our annotation tool is supported in three major operating systems (Windows, Linux, and the Mac OS X). The quantitative results comparing our free software with commercially available lung segmentation tools show higher degree of consistency and precision of our software with a considerable potential to enhance the performance of routine clinical tasks.

Pulmonary lymphangioleiomyomatosis: analysis of disease manifestation by region-based quantification of lung parenchyma

PURPOSE

Lymphangioleiomyomatosis (LAM) is characterized by proliferation of smooth muscle tissue that causes bronchial obstruction and secondary cystic destruction of lung parenchyma. The aim of this study was to evaluate the typical distribution of cystic defects in LAM with quantitative volumetric chest computed tomography (CT).

MATERIALS AND METHODS

CT examinations of 20 patients with confirmed LAM were evaluated with region-based quantification of lung parenchyma. Additionally, 10 consecutive patients were identified who had recently undergone CT imaging of the lung at our institution, in which no pathologies of the lung were found, to serve as a control group. Each lung was divided into three regions (upper, middle and lower thirds) with identical number of slices. In addition, we defined a "peel" and "core" of the lung comprising the 2 cm subpleural space and the remaining inner lung area. Computerized detection of lung volume and relative emphysema was performed with the PULMO 3D software (v3.42, Fraunhofer MEVIS, Bremen, Germany). This software package enables the quantification of emphysematous lung parenchyma by calculating the pixel index, which is defined as the ratio of lung voxels with a density <-950HU to the total number of voxels in the lung.

RESULTS

Cystic changes accounted for 0.1-39.1% of the total lung volume in patients with LAM. Disease manifestation in the central lung was significantly higher than in peripheral areas (peel median: 15.1%, core median: 20.5%; p=0.001). Lower thirds of lung parenchyma showed significantly less cystic changes than upper and middle lung areas combined (lower third: median 13.4, upper and middle thirds: median 19.0, p=0.001).

CONCLUSION

The distribution of cystic lesions in LAM is significantly more pronounced in the central lung compared to peripheral areas. There is a significant predominance of cystic changes in apical and intermediate lung zones compared to the lung bases.

A generic approach to pathological lung segmentation

In this study, we propose a novel pathological lung segmentation method that takes into account neighbor prior constraints and a novel pathology recognition system. Our proposed framework has two stages; during stage one, we adapted the fuzzy connectedness (FC) image segmentation algorithm to perform initial lung parenchyma extraction. In parallel, we estimate the lung volume using rib-cage information without explicitly delineating lungs. This rudimentary, but intelligent lung volume estimation system allows comparison of volume differences between rib cage and FC based lung volume measurements. Significant volume difference indicates the presence of pathology, which invokes the second stage of the proposed framework for the refinement of segmented lung. In stage two, texture-based features are utilized to detect abnormal imaging patterns (consolidations, ground glass, interstitial thickening, tree-inbud, honeycombing, nodules, and micro-nodules) that might have been missed during the first stage of the algorithm. This refinement stage is further completed by a novel neighboring anatomy-guided segmentation approach to include abnormalities with weak textures, and pleura regions. We evaluated the accuracy and efficiency of the proposed method on more than 400 CT scans with the presence of a wide spectrum of abnormalities. To our best of knowledge, this is the first study to evaluate all abnormal imaging patterns in a single segmentation framework. The quantitative results show that our pathological lung segmentation method improves on current standards because of its high sensitivity and specificity and may have considerable potential to enhance the performance of routine clinical tasks.

Quantitative Emphysema Distribution in Anatomic and Non-anatomic Lung Regions

PURPOSE

The change of emphysema distribution with increasing COPD severity is not yet assessed. Especially, involvement of the upper aspect of the lower lobe is unknown. The primary aim was to quantitatively determine regional distribution of emphysema in anatomically (lung lobes) and non-anatomically defined lung regions (upper/lower lung halves as well as core and rind regions) in a cohort covering equally all COPD severity stages using CT.

MATERIAL AND METHODS

Basically 100 CT data sets were quantitatively evaluated for regional distribution of emphysema. Emphysema characteristics (emphysema index, mean lung density and 15th percentile of the attenuation values of lung voxels) were compared (t-test) in: upper lobes vs. upper halves, lower lobes vs. lower halves, core vs. rind region.

RESULTS

In patients with ≤ GOLD II, a significantly higher emphysema burden was found in the upper lobes as compared to upper halves. In subjects with GOLD III/IV the differences were not significant for all emphysema characteristics. A high difference between lobes and halves in subjects with ≤ GOLD II was found, in contrast to low difference in higher GOLD stages.

CONCLUSIONS

Lobar segmentation provides improved characterization of cranio-caudal emphysema distribution compared to a non-anatomic approach in subjects up to GOLD stage II.

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.

Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study

The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website http://vessel12.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases.

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.

Bronchial wall measurements in patients after lung transplantation: evaluation of the diagnostic value for the diagnosis of bronchiolitis obliterans syndrome

OBJECTIVES

To prospectively evaluate quantitative airway wall measurements of thin-section CT for the diagnosis of Bronchiolitis Obliterans Syndrome (BOS) following lung transplantation.

MATERIALS AND METHODS

In 141 CT examinations, bronchial wall thickness (WT), the wall area percentage (WA%) calculated as the ratio of the bronchial wall area and the total area (sum of bronchial wall area and bronchial lumen area) and the difference of the WT on inspiration and expiration (WTdiff) were automatically measured in different bronchial generations. The measurements were correlated with the lung function parameters. WT and WA% in CT examinations of patients with (n = 25) and without (n = 116) BOS, were compared using the unpaired t-test and univariate analysis of variance, while also considering the differing lung volumes.

RESULTS

Measurements could be performed in 2,978 bronchial generations. WT, WA%, and WTdiff did not correlate with the lung function parameters (r<0.5). The WA% on inspiration was significantly greater in patients with BOS than in patients without BOS, even when considering the dependency of the lung volume on the measurements. WT on inspiration and expiration and WA% on expiration did not show significant differences between the groups.

CONCLUSION

WA% on inspiration was significantly greater in patients with than in those without BOS. However, WA% measurements were significantly dependent on lung volume and showed a high variability, thus not allowing the sole use of bronchial wall measurements to differentiate patients with from those without BOS.

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.

Increased mean lung density: another independent predictor of lung cancer?

OBJECTIVES

To investigate the relationship between emphysema phenotype, mean lung density (MLD), lung function and lung cancer by using an automated multiple feature analysis tool on thin-section computed tomography (CT) data.

METHODS

Both emphysema phenotype and MLD evaluated by automated quantitative CT analysis were compared between outpatients and screening participants with lung cancer (n=119) and controls (n=989). Emphysema phenotype was defined by assessing features such as extent, distribution on core/peel of the lung and hole size. Adjusted multiple logistic regression models were used to evaluate independent associations of CT densitometric measurements and pulmonary function test (PFT) with lung cancer risk.

RESULTS

No emphysema feature was associated with lung cancer. Lung cancer risk increased with decreasing values of forced expiratory volume in 1s (FEV1) independently of MLD (OR 5.37, 95% CI: 2.63-10.97 for FEV1<60% vs. FEV1≥90%), and with increasing MLD independently of FEV1 (OR 3.00, 95% CI: 1.60-5.63 for MLD>-823 vs. MLD<-857 Hounsfield units).

CONCLUSION

Emphysema per se was not associated with lung cancer whereas decreased FEV1 was confirmed as being a strong and independent risk factor. The cross-sectional association between increased MLD and lung cancer requires future validations.

Comparison of human lung tissue mass measurements from ex vivo lungs and high resolution CT software analysis

Background

Quantification of lung tissue via analysis of computed tomography (CT) scans is increasingly common for monitoring disease progression and for planning of therapeutic interventions. The current study evaluates the quantification of human lung tissue mass by software analysis of a CT to physical tissue mass measurements.

Methods

Twenty-two ex vivo lungs were scanned by CT and analyzed by commercially available software. The lungs were then dissected into lobes and sublobar segments and weighed. Because sublobar boundaries are not visually apparent, a novel technique of defining sublobar segments in ex vivo tissue was developed. The tissue masses were then compared to measurements by the software analysis.

Results

Both emphysematous (n = 14) and non-emphysematous (n = 8) bilateral lungs were evaluated. Masses (Mean ± SD) as measured by dissection were 651 ± 171 g for en bloc lungs, 126 ± 60 g for lobar segments, and 46 ± 23 g for sublobar segments. Masses as measured by software analysis were 598 ± 159 g for en bloc lungs, 120 ± 58 g for lobar segments, and 45 ± 23 g for sublobar segments. Correlations between measurement methods was above 0.9 for each segmentation level. The Bland-Altman analysis found limits of agreement at the lung, lobe and sublobar levels to be −13.11% to −4.22%, –13.59% to 4.24%, and –45.85% to 44.56%.

Conclusion

The degree of concordance between the software mass quantification to physical mass measurements provides substantial evidence that the software method represents an appropriate non-invasive means to determine lung tissue mass.

Relationship and prognostic value of modified coronary artery calcium score, FEV1, and emphysema in lung cancer screening population: the MILD trial

PURPOSE

To assess the relationship between a modified coronary artery calcium (mCAC) score and both forced expiratory volume in 1 second (FEV1) and pulmonary emphysema and the associations of such factors with all-cause mortality and cardiovascular events (CVEs) in a lung cancer computed tomographic (CT) screening trial.

MATERIALS AND METHODS

In this institutional review board-approved study, both clinical and low-dose CT data were evaluated in a cohort of heavy smokers consecutively recruited by the Multicentric Italian Lung Detection, or MILD, trial. Low-dose CT images were analyzed by using software that allowed quantification of mCAC, mean lung attenuation (MLA), and total extent of emphysema. The correlations between mCAC, percentage predicted FEV1, MLA, and emphysema extent were tested by using the Pearson correlation coefficient. Adjusted multiple logistic regression models were applied to assess the relationships between mCAC, FEV1, MLA, and emphysema extent and all-cause mortality and CVEs.

RESULTS

The final study cohort consisted of 1159 smokers. There were no significant correlations between mCAC score and FEV1 (r=-0.03, P=.4), MLA (r=-0.01, P=.7), or emphysema extent (r=0.02, P=.6). An mCAC score greater than 400 was the only factor that was independently associated with both all-cause mortality (odds ratio [OR]: 3.73; 95% confidence interval [CI]: 1.05, 13.32; P=.04) and CVEs (OR: 2.87; 95% CI: 1.13, 7.27; P=.03).

CONCLUSION

mCAC is a better predictor of CVE and all-cause mortality than FEV1 and emphysema extent and may contribute to the identification of high-risk individuals in a lung cancer screening setting.

Defining the intra-subject variability of whole-lung CT densitometry in two lung cancer screening trials

RATIONALE AND OBJECTIVES

To define a statistically based variation of individual whole-lung densitometry above which a real increase of pulmonary extent can be suspected in lung cancer screening trials.

MATERIALS AND METHODS

Baseline and 3-month follow-up low-dose computed tomography (LDCT) examinations of 131 smokers or former smokers recruited in the ITALUNG (32 subjects) and MILD (99 subjects) trials were compared using for each data set two different image processing tools for whole-lung densitometry. Both trials were approved by institutional review boards, and written informed consent was obtained from all participants. Assuming that no change of emphysema extent can occur in a 3-month interval, the Bland and Altman method was used to assess the agreement between baseline and follow-up LDCT examinations for lung volume, 15th percentile (Perc15) of lung density and Perc15 corrected for lung volume by application of a linear detrend on log-transformed data.

RESULTS

Similar results were obtained in each data set using two different image processing tools. In the ITALUNG cohort the 95% limits of agreement (LoA) interval of volume corrected Perc15 was -9.7 to 10.7% using image processing method 1 and -10.3 to 11.5% using image processing method 2. In the MILD cohort, the 95% LoA interval of volume corrected Perc15 was -14.7 to 17.3% with both image processing methods.

CONCLUSION

In the two considered lung cancer screening settings a range of 9.7-14.7% decrease of volume corrected Perc15 represents a statistically defined threshold to suspect a real increase of emphysema extent in serial LDCT examinations.

A method for the automatic quantification of the completeness of pulmonary fissures: evaluation in a database of subjects with severe emphysema

OBJECTIVES

To propose and evaluate a technique for automatic quantification of fissural completeness from chest computed tomography (CT) in a database of subjects with severe emphysema.

METHODS

Ninety-six CT studies of patients with severe emphysema were included. The lungs, fissures and lobes were automatically segmented. The completeness of the fissures was calculated as the percentage of the lobar border defined by a fissure. The completeness score of the automatic method was compared with a visual consensus read by three radiologists using boxplots, rank sum tests and ROC analysis.

RESULTS

The consensus read found 49% (47/96), 15% (14/96) and 67% (64/96) of the right major, right minor and left major fissures to be complete. For all fissures visually assessed as being complete the automatic method resulted in significantly higher completeness scores (mean 92.78%) than for those assessed as being partial or absent (mean 77.16%; all p values <0.001). The areas under the curves for the automatic fissural completeness were 0.88, 0.91 and 0.83 for the right major, right minor and left major fissures respectively.

CONCLUSIONS

An automatic method is able to quantify fissural completeness in a cohort of subjects with severe emphysema consistent with a visual consensus read of three radiologists.

KEY POINTS

• Lobar fissures are important for assessing the extent and distribution of lung disease • Modern CT allows automatic lobar segmentation and assessment of the fissures • This segmentation can also assess the completeness of the fissures. • Such assessment is important for decisions about novel therapies (eg for emphysema).

Pulmonary emphysematous changes in patients with congenital heart disease associated with increased pulmonary blood flow: evaluation using multidetector-row computed tomography

BACKGROUND

The present study aimed to evaluate the prevalence and the location of segmental emphysematous change in congenital heart disease (CHD) patients with increased pulmonary blood flow using multidetector-row computed tomography (MDCT).

METHODS

A total of 129 consecutive patients (mean age, 5.8±5.4 years; range, 1 month to 24 years) underwent MDCT angiography of the thorax. The frequency of emphysematous change was evaluated in patients with ventricular septal defect (VSD, n=61), atrial septal defect (ASD, n=27), patent ductus arteriosus (PDA, n=36) and complete atriventriclar septal defect (CAVSD, n=5). In 59 patients who underwent cardiac catheterisation, the relationships between the emphysematous change and both pulmonary to systemic blood flow ratio (Qp/Qs) and mean pulmonary arterial pressure (mPAP) were evaluated.

RESULTS

The emphysematous change was detected in 57 patients (44.2%) out of 129 patients. The frequency of segmental emphysematous change in left side was higher than in right side (14.8% vs. 6.5%). Both Qp/Qs and mPAP affected the presence of emphysema.

CONCLUSION

MDCT can provide accurate detection of segmental emphysema in patients with CHD. Emphysematous change is not uncommon pathological lesion in children and adolescents with CHD.

Computed tomography and magnetic resonance imaging in cystic fibrosis lung disease

Computed tomography (CT) is the current "gold standard" for assessment of lung morphology and is so far the most reliable imaging modality for monitoring cystic fibrosis (CF) lung disease. CT has a much higher radiation exposure than chest x-ray. The cumulative radiation dose for life-long repeated CT scans has limited its use for CF patients as their life expectancy increases. Clearly, no dose would be preferable over low dose when the same or more relevant information can be obtained. Magnetic resonance imaging (MRI) is comparable to CT with regard to the detection of most morphological changes in the CF lung. It is thought to be less sensitive to detect small airway disease. At the same time, MRI is superior to CT when it comes to the assessment of functional changes such as altered pulmonary perfusion. The recommendation is to further reduce radiation dose related to the use of CT and to use MRI in the follow-up of morphological changes where possible.