Search

This study aims to accurately classify the airways in the human respiratory system, which are characterized by diverse pattern forming a complex tree-like structure. Although recent advances in deep learning show promise for automatic airway segmentation, further research is needed to make it practical for determining a patient’s airway status for tailored treatment. To enhance diagnostics and treatments, it’s crucial to delve into the study of airway generation. This exploration helps in understanding specific structures and pinpointing issues like airway narrowing and lung compliance. Current methods, such as template-matching and machine learning based methods that interpret this as classification problem, have limitations in capturing the complexity of higher-generation airways. To overcome these challenges, our study proposes a novel approach. The Prim algorithm initially establishes a minimum spanning tree from the centerline to create an accurate tree structure that reflects airway connections. A new secondary branch is formed when a main branch with an outdegree of 1 is detected, ensuring precise airway generation labeling. To mitigate false branch generation, the study proposes an approach that increases the cost of trunk lines connected to a centerline with an outdegree of 1. Additionally, a method for pruning trees based on subtree length is proposed to effectively handle segmentation results in deep learning. This method prevents the generation of false branches by removing vertices with shorter subtree airway lengths than their siblings. The approach successfully addresses the common issue of false branch generation, providing reliable labeling of airway generation in higher-generation sections. In conclusion, the technique we propose offers substantial benefits for patient health monitoring, disease prediction, and prevention. It achieves this by providing precise and dependable identification of airway generations within the intricate anatomy of the respiratory system.

Objective: To develop and validate convolutional neural network algorithms for automatic upper airway segmentation and minimum cross-sectional area (CSAmin) localisation in two-dimensional (2D) radiographic airway images. Materials and Methods: Two hundred and one 2D airway images acquired using cone-beam computed tomography (CBCT) scanning were randomly assigned to a test group (n = 161) to train artificial intelligence (AI) models and a validation group (n = 40) to evaluate the accuracy of AI processing. Four AI models, UNet18, UNet36, DeepLab50 and DeepLab101, were trained to automatically segment the upper airway 2D images in the test group. Precision, recall, Intersection over Union, the dice similarity coefficient and size difference were used to evaluate the performance of the AI-driven segmentation models. The CSAmin height in each image was manually determined using three-dimensional CBCT data. The nonlinear mathematical morphology technique was used to calculate the CSAmin level. Height errors were assessed to evaluate the CSAmin localisation accuracy in the validation group. The time consumed for airway segmentation and CSAmin localisation was compared between manual and AI processing methods. Results: The precision of all four segmentation models exceeded 90.0%. No significant differences were found in the accuracy of any AI models. The consistency of CSAmin localisation in specific segments between manual and AI processing was 0.944. AI processing was much more efficient than manual processing in terms of airway segmentation and CSAmin localisation. Conclusions: We successfully developed and validated a fully automatic AI-driven system for upper airway segmentation and CSAmin localisation using 2D radiographic airway images. [ABSTRACT FROM AUTHOR]

Automated and accurate airway segmentation from chest computed tomography (CT) images is essential to enable quantitative assessment of airway diseases and aid intra-operative navigation for pulmonary intervention surgery. Although deep learning-based methods have achieved massive success in medical image segmentation, it is still challenging to segment the airways accurately and entirely from CT images, especially the small airways. The feature vanishing of small airways, the local discontinuities of small airway branches, and the varying degrees of class imbalance between foreground and background have seriously affected airway segmentation performance. This paper presents an improved UNet++-based model that introduces a novel supervision manner and a new adaptive loss function to address these problems. Specifically, we put forward an attentional context supervision (ACS) manner, where different supervision branches and attention mechanisms are presented to capture more discriminative multi-scale features. In addition, we present an adaptive Tversky loss (ATL) function by integrating radial distance information and segmentation-wise focal loss into the Tversky loss, enabling adaptive focus on learning the target airways under the particular class imbalance condition. The experimental results on the public dataset showed that the proposed ACS and ATL brought considerable performance gains. Moreover, our method obtained the best sensitivity and comparable accuracy on the complete airway segmentation compared with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

• This paper investigates the capacity of AI models for airway modelling on national datasets with paired clinical metadata. • We evaluated AI models against unharmonised, noisy, and out-of-distribution data, as well as the prognostication for FLD. • We found a new biomarker for mortality prediction, outperforming existing clinical measurements (FVC% and fibrosis scores). • In-depth analysis of AI models on airway modelling and prognosis, highlighting challenges and future research directions. Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway structures remains prohibitively time-consuming. While significant efforts have been made towards enhancing automatic airway modelling, current public-available datasets predominantly concentrate on lung diseases with moderate morphological variations. The intricate honeycombing patterns present in the lung tissues of fibrotic lung disease patients exacerbate the challenges, often leading to various prediction errors. To address this issue, the 'Airway-Informed Quantitative CT Imaging Biomarker for Fibrotic Lung Disease 2023′ (AIIB23) competition was organized in conjunction with the official 2023 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). The airway structures were meticulously annotated by three experienced radiologists. Competitors were encouraged to develop automatic airway segmentation models with high robustness and generalization abilities, followed by exploring the most correlated QIB of mortality prediction. A training set of 120 high-resolution computerised tomography (HRCT) scans were publicly released with expert annotations and mortality status. The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients. The results have shown that the capacity of extracting airway trees from patients with fibrotic lung disease could be enhanced by introducing voxel-wise weighted general union loss and continuity loss. In addition to the competitive image biomarkers for mortality prediction, a strong airway-derived biomarker (Hazard ratio>1.5, p < 0.0001) was revealed for survival prognostication compared with existing clinical measurements, clinician assessment and AI-based biomarkers. [Display omitted] [ABSTRACT FROM AUTHOR]

Objective: To develop and validate convolutional neural network algorithms for automatic upper airway segmentation and minimum cross-sectional area (CSAmin) localisation in two-dimensional (2D) radiographic airway images. Materials and Methods: Two hundred and one 2D airway images acquired using cone-beam computed tomography (CBCT) scanning were randomly assigned to a test group (n = 161) to train artificial intelligence (AI) models and a validation group (n = 40) to evaluate the accuracy of AI processing. Four AI models, UNet18, UNet36, DeepLab50 and DeepLab101, were trained to automatically segment the upper airway 2D images in the test group. Precision, recall, Intersection over Union, the dice similarity coefficient and size difference were used to evaluate the performance of the AI-driven segmentation models. The CSAmin height in each image was manually determined using three-dimensional CBCT data. The nonlinear mathematical morphology technique was used to calculate the CSAmin level. Height errors were assessed to evaluate the CSAmin localisation accuracy in the validation group. The time consumed for airway segmentation and CSAmin localisation was compared between manual and AI processing methods. Results: The precision of all four segmentation models exceeded 90.0%. No significant differences were found in the accuracy of any AI models. The consistency of CSAmin localisation in specific segments between manual and AI processing was 0.944. AI processing was much more efficient than manual processing in terms of airway segmentation and CSAmin localisation. Conclusions: We successfully developed and validated a fully automatic AI-driven system for upper airway segmentation and CSAmin localisation using 2D radiographic airway images.

In the realm of pulmonary tracheal segmentation, the scarcity of annotated data stands as a prevalent pain point in most medical segmentation endeavors. Concurrently, most Deep Learning (DL) methodologies employed in this domain invariably grapple with other dual challenges: the inherent opacity of 'black box' models and the ongoing pursuit of performance enhancement. In response to these intertwined challenges, the core concept of our Human-Computer Interaction (HCI) based learning models (RS_UNet, LC_UNet, UUNet and WD_UNet) hinge on the versatile combination of diverse query strategies and an array of deep learning models. We train four HCI models based on the initial training dataset and sequentially repeat the following steps 1–4: (1) Query Strategy: Our proposed HCI models selects those samples which contribute the most additional representative information when labeled in each iteration of the query strategy (showing the names and sequence numbers of the samples to be annotated). Additionally, in this phase, the model selects the unlabeled samples with the greatest predictive disparity by calculating the Wasserstein Distance, Least Confidence, Entropy Sampling, and Random Sampling. (2) Central line correction: The selected samples in previous stage are then used for domain expert correction of the system-generated tracheal central lines in each training round. (3) Update training dataset: When domain experts are involved in each epoch of the DL model's training iterations, they update the training dataset with greater precision after each epoch, thereby enhancing the trustworthiness of the 'black box' DL model and improving the performance of models. (4) Model training: Proposed HCI model is trained using the updated training dataset and an enhanced version of existing UNet. Experimental results validate the effectiveness of this Human-Computer Interaction-based approaches, demonstrating that our proposed WD-UNet, LC-UNet, UUNet, RS-UNet achieve comparable or even superior performance than the state-of-the-art DL models, such as WD-UNet with only 15 %–35 % of the training data, leading to substantial reductions (65 %–85 % reduction of annotation effort) in physician annotation time. • Application of Human-AI collaboration within our deep learning framework. Drastic reduction in the total amount of data to be labelled, leading to significant cost savings on annotation. Expert intervention in the machine learning training process allows for the efficient labelling of the most uncertain samples. • Utilization of the domain knowledge and experience of radiology experts for trachea annotation and correction of machine-generated tracheal centrelines. This addresses the issue of inaccurate intermediate results during initial iterations of model training. • Integration of domain knowledge from experts into our Human-Computer Interaction(HCI) based learning framework, resulting in a substantial enhancement of model performance. • Remarkable results with only 15%-35% of the training data when employing the Human-Computer Interaction based learning model, achieving performance equivalent to a supervised learning model (e.g., U-Net) that uses all available training data. • High flexibility and adaptability in the Human-Computer Interaction based learning framework, as network models and HCI query strategies can be replaced with various alternatives, offering robust reproducibility and diverse selection choices. [ABSTRACT FROM AUTHOR]

Purpose: Bronchoscopic intervention is a widely used clinical technique for pulmonary diseases, which requires an accurate and topological complete airway map for its localization and guidance. The airway map could be extracted from chest computed tomography (CT) scans automatically by airway segmentation methods. Due to the complex tree-like structure of the airway, preserving its topology completeness while maintaining the segmentation accuracy is a challenging task. Methods: In this paper, a long-term slice propagation (LTSP) method is proposed for accurate airway segmentation from pathological CT scans. We also design a two-stage end-to-end segmentation framework utilizing the LTSP method in the decoding process. Stage 1 is used to generate a coarse feature map by an encoder–decoder architecture. Stage 2 is to adopt the proposed LTSP method for exploiting the continuity information and enhancing the weak airway features in the coarse feature map. The final segmentation result is predicted from the refined feature map. Results: Extensive experiments were conducted to evaluate the performance of the proposed method on 70 clinical CT scans. The results demonstrate the considerable improvements of the proposed method compared to some state-of-the-art methods as most breakages are eliminated and more tiny bronchi are detected. The ablation studies further confirm the effectiveness of the constituents of the proposed method and the efficacy of the framework design. Conclusion: Slice continuity information is beneficial to accurate airway segmentation. Furthermore, by propagating the long-term slice feature, the airway topology connectivity is preserved with overall segmentation accuracy maintained. [ABSTRACT FROM AUTHOR]

Automated airway segmentation is a prerequisite for pre-operative diagnosis and intra-operative navigation for pulmonary intervention. Due to the small size and scattered spatial distribution of peripheral bronchi, this is hampered by a severe class imbalance between foreground and background regions, which makes it challenging for CNN-based methods to parse distal small airways. In this paper, we demonstrate that this problem is arisen by gradient erosion and dilation of the neighborhood voxels. During back-propagation, if the ratio of the foreground gradient to background gradient is small while the class imbalance is local, the foreground gradients can be eroded by their neighborhoods. This process cumulatively increases the noise information included in the gradient flow from top layers to the bottom ones, limiting the learning of small structures in CNNs. To alleviate this problem, we use group supervision and the corresponding WingsNet to provide complementary gradient flows to enhance the training of shallow layers. To further address the intra-class imbalance between large and small airways, we design a General Union loss function that obviates the impact of airway size by distance-based weights and adaptively tunes the gradient ratio based on the learning process. Extensive experiments on public datasets demonstrate that the proposed method can predict the airway structures with higher accuracy and better morphological completeness than the baselines. [ABSTRACT FROM AUTHOR]

Objective . Automatic and accurate airway segmentation is necessary for lung disease diagnosis. The complex tree-like structures leads to gaps in the different generations of the airway tree, and thus airway segmentation is also considered to be a multi-scale problem. In recent years, convolutional neural networks have facilitated the development of medical image segmentation. In particular, 2D CNNs and 3D CNNs can extract different scale features. Hence, we propose a two-stage and 2D + 3D framework for multi-scale airway tree segmentation. Approach . In stage 1, we use a 2D full airway SegNet(2D FA-SegNet) to segment the complete airway tree. Multi-scale atros spatial pyramid and Atros Residual Skip connection modules are inserted to extract different scales feature. We designed a hard sample selection strategy to increase the proportion of intrapulmonary airway samples in stage 2. 3D airway RefineNet (3D ARNet) as stage 2 takes the results of stage 1 as a priori information. Spatial information extracted by 3D convolutional kernel compensates for the loss of in 2D FA-SegNet. Furthermore, we added false positive losses and false negative losses to improve the segmentation performance of airway branches within the lungs. Main results . We performed data enhancement on the publicly available dataset of ISICDM 2020 Challenge 3, and on which evaluated our method. Comprehensive experiments show that the proposed method has the highest dice similarity coefficient (DSC) of 0.931, and IoU of 0.871 for the whole airway tree and DSC of 0.699, and IoU of 0.543 for the intrapulmonary bronchi tree. In addition, 3D ARNet proposed in this paper cascaded with other state-of-the-art methods to increase detected tree length rate by up to 46.33% and detected tree branch rate by up to 42.97%. Significance . The quantitative and qualitative evaluation results show that our proposed method performs well in segmenting the airway at different scales., (© 2024 Institute of Physics and Engineering in Medicine.)

Purpose: The aim of this study was to evaluate the advantages of ultra-high-resolution computed tomography (U-HRCT) for automatic bronchial segmentation. Materials and methods: This retrospective study was approved by the Institutional Review Board, and written informed consent was waived. Thirty-three consecutive patients who underwent chest CT by a U-HRCT scanner were enrolled. In each patient, CT data were reconstructed by two different protocols: 512 × 512 matrix with 0.5-mm slice thickness (conventional HRCT mode) and 1024 × 1024 matrix with 0.25-mm slice thickness (U-HRCT mode). We used a research workstation to compare the two CT modes with regard to the numbers and total lengths of the automatically segmented bronchi. Results: Significantly greater numbers and longer lengths of peripheral bronchi were segmented in the U-HRCT mode than in the conventional HRCT mode (P < 0.001, for fifth- to eighth-generation bronchi). For example, the mean numbers and total lengths of the sixth-generation bronchi were 81 and 1048 mm in the U-HRCT mode and 59 and 538 mm in the conventional HRCT mode. Conclusions: The U-HRCT mode greatly improves automatic airway segmentation for the more peripheral bronchi, compared with the conventional HRCT mode. This advantage can be applied to routine clinical care, such as virtual bronchoscopy and automatic lung segmentation. [ABSTRACT FROM AUTHOR]

Background: Pulmonary segments are valuable because they can provide more precise localization and intricate details of lung cancer than lung lobes. With advances in precision therapy, there is an increasing demand for the identification and visualization of pulmonary segments in computed tomography (CT) images to aid in the precise treatment of lung cancer. This study aimed to integrate multiple deep-learning models to accurately segment pulmonary segments in CT images using a bronchial tree (BT)-based approach., Methods: The proposed segmentation method for pulmonary segments using the BT-based approach comprised the following five essential steps: (I) segmentation of the lung using a U-Net (R231) (public access) model; (II) segmentation of the lobes using a V-Net (self-developed) model; (III) segmentation of the airway using a combination of a differential geometric approach method and a BronchiNet (public access) model; (IV) labeling of the BT branches based on anatomical position; and (V) segmentation of the pulmonary segments based on the distance of each voxel to the labeled BT branches. This five-step process was applied to 14 high-resolution breath-hold CT images and compared against manual segmentations for evaluation., Results: For the lung segmentation, the lung mask had a mean dice similarity coefficient (DSC) of 0.98±0.03. For the lobe segmentation, the V-Net model had a mean DSC of 0.94±0.06. For the airway segmentation, the average total length of the segmented airway trees per image scan was 1,902.8±502.1 mm, and the average number of the maximum airway tree generations was 8.5±1.3. For the segmentation of the pulmonary segments, the proposed method had a DSC of 0.73±0.11 and a mean surface distance of 6.1±2.9 mm., Conclusions: This study demonstrated the feasibility of combining multiple deep-learning models for the auxiliary segmentation of pulmonary segments on CT images using a BT-based approach. The results highlighted the potential of the BT-based method for the semi-automatic segmentation of the pulmonary segment., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-23-1251/coif). The authors have no conflicts of interest to declare., (2024 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Airway diseases are frequently related to morphological changes that may influence lung physiology. Accurate airway region segmentation may be useful for quantitative evaluation of disease prognosis and therapy efficacy. The information can also be applied to understand the fundamental mechanisms of various lung diseases. We present a hybrid method to automatically segment the airway regions on 3D volume chest computed tomography (CT) scans. This method uses multi-scale filtering and support vector machine (SVM) classification. The proposed scheme is comprised of two hybrid steps. First, a tubular structure-based multi-scale filter is applied to find the initial candidate airway regions. Second, for identifying candidate airway regions using the fuzzy connectedness technique, the small and disconnected branches of airway regions are detected using SVM classification trained to differentiate between airway and non-airway regions through texture analysis of user-defined landmark points. For development and evaluation of the method, two datasets were incorporated: (1) 55 lung-CT volumes from the Korean Obstructive Lung Disease (KOLD) Cohort Study and (2) 20 cases from the publicly open database (EXACT′09). The average tree-length detection rates of EXACT′09 and KOLD were 56.9 ± 11.0 and 70.5 ± 8.98, respectively. Comparison of the results for the EXACT′09 data set between the presented method and other methods revealed that our approach was a high performer. The method limitations were higher false-positive rates than those of the other methods and risk of leakage. In future studies, application of a convolutional neural network will help overcome these shortcomings. [ABSTRACT FROM AUTHOR]

Chest wall strapping (CWS) induces breathing at low lung volumes but also increases parenchymal elastic recoil. In this study, we tested the hypothesis that CWS dilates airways via airway-parenchymal interdependence. In 11 subjects (6 healthy and 5 with mild to moderate COPD), pulmonary function tests and lung volumes were obtained in control (baseline) and the CWS state. Control and CWS-CT scans were obtained at 50% of control (baseline) total lung-capacity (TLC). CT lung volumes were analyzed by CT volumetry. If control and CWS-CT volumetry did not differ by more than 25%, airway dimensions were analyzed via automated airway segmentation. CWS-TLC was reduced on average to 71% of control-TLC in normal subjects and 79% of control-TLC in subjects with COPD. CWS increased expiratory airflow at 50% of control-TLC by 41% (3.50 ± 1.6 vs. 4.93 ± 1.9 l/s, P = 0.04) in normals and 316% in COPD(0.25 ± 0.05 vs 0.79 ± 0.39 l/s, P = 0.04). In 10 subjects (5 normals and 5 COPD), control and CWS-CT scans at 50% control-TLC did not differ more than 25% on CT volumetry and were included in the airway structure analysis. CWS increased the mean number of detectable airways with a diameter of ≤2 mm by 32.5% (65 ± 10 vs. 86 ± 124, P = 0.01) in normal subjects and by 79% (59 ± 19 vs. 104 ± 16, P = 0.01) in subjects with COPD. There was no difference in the number of detectable airways with diameters 2-4 mm and >4 mm in normal or in COPD subjects. In conclusion, CWS enhances the detection of small airways via automated CT airway segmentation and increases expiratory airflow in normal subjects as well as in subjects with mild to moderate COPD. NEW & NOTEWORTHY In normal and COPD subjects, chest wall strapping(CWS) increased the number of detectable small airways using automated CT airway segmentation. The concept of dysanapsis expresses the physiological variation in the geometry of the tracheobronchial tree and lung parenchyma based on development. We propose a dynamic concept to dysanapsis in which CWS leads to breathing at lower lung volumes with a corresponding increase in the size of small airways, a potentially novel, nonpharmacological treatment for COPD. [ABSTRACT FROM AUTHOR]

We propose a novel method to improve airway segmentation in thoracic computed tomography (CT) by detecting and removing leaks. Leak detection is formulated as a classification problem, in which a convolutional network (ConvNet) is trained in a supervised fashion to perform the classification task. In order to increase the segmented airway tree length, we take advantage of the fact that multiple segmentations can be extracted from a given airway segmentation algorithm by varying the parameters that influence the tree length and the amount of leaks. We propose a strategy in which the combination of these segmentations after removing leaks can increase the airway tree length while limiting the amount of leaks. This strategy therefore largely circumvents the need for parameter fine-tuning of a given airway segmentation algorithm. The ConvNet was trained and evaluated using a subset of inspiratory thoracic CT scans taken from the COPDGene study. Our method was validated on a separate independent set of the EXACT’09 challenge. We show that our method significantly improves the quality of a given leaky airway segmentation, achieving a higher sensitivity at a low false-positive rate compared to all the state-of-the-art methods that entered in EXACT09, and approaching the performance of the combination of all of them. [ABSTRACT FROM AUTHOR]

Purpose: Lung cancer still represents the leading cause of cancer-related death, and the long-term survival rate remains low. Computed tomography (CT) is currently the most common imaging modality for lung diseases recognition. The purpose of this work was to develop a simple and easily accessible virtual bronchoscopy system to be coupled with a customized electromagnetic (EM) tracking system for navigation in the lung and which requires as little user interaction as possible, while maintaining high usability. Methods: The proposed method has been implemented as an extension to the open-source platform, 3D Slicer. It creates a virtual reconstruction of the airways starting from CT images for virtual navigation. It provides tools for pre-procedural planning and virtual navigation, and it has been optimized for use in combination with a $$5^{\circ }$$ of freedom EM tracking sensor. Performance of the algorithm has been evaluated in ex vivo and in vivo testing. Results: During ex vivo testing, nine volunteer physicians tested the implemented algorithm to navigate three separate targets placed inside a breathing pig lung model. In general, the system proved easy to use and accurate in replicating the clinical setting and seemed to help choose the correct path without any previous experience or image analysis. Two separate animal studies confirmed technical feasibility and usability of the system. Conclusions: This work describes an easily accessible virtual bronchoscopy system for navigation in the lung. The system provides the user with a complete set of tools that facilitate navigation towards user-selected regions of interest. Results from ex vivo and in vivo studies showed that the system opens the way for potential future work with virtual navigation for safe and reliable airway disease diagnosis. [ABSTRACT FROM AUTHOR]

Background: Airway segmentation is a way to quantify the diagnosis of pulmonary diseases, including chronic obstructive problems and bronchiectasis. Manual analysis by radiologists is a challenging task due to the complex airway structure. Additionally, tree-like patterns, varied shapes, sizes, and intensity make the manual airway segmentation task more complex. Deeper airways are even more difficult to segment as their intensity starts matching the lung parenchyma as the diameter of the airway cross-section gets reduced., Objective: Many earlier works have proposed different deep learning networks for airway segmentation but were unable to achieve the desired performance; hence the task of airway segmentation still possesses challenges in this field., Methods: This work proposes a convolutional neural network based on deep U-Net architecture and employs an attention block technique for airway segmentation. The attention mechanism aids in the extraction of the complicated and multi-sized airways found in the lung region, hence increasing the efficiency of the U-Net architecture., Results: The model has been validated using VESSEL12 and EXACT09 datasets, individually and combined, with and without trachea images. The best DSC scores on EXACT09 and VESSEL12 datasets are 95.21% and 95.80%, respectively. The performance on both datasets combined gave a DSC score of 94.1%, showing that the overall performance of the proposed methodology is quite satisfactory. The generalizability of the model is also confirmed using k-fold cross-validation. The comparison of the proposed model to existing research on airway segmentation found competitive results., Conclusion: The use of an attention unit in the proposed model highlights the relevant information and reduces the irrelevant features, which helps to improve the performance and saves time., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Background: Computed tomography (CT) helps physicians locate and diagnose pathological conditions. In some conditions, having an airway segmentation method which facilitates reconstruction of the airway from chest CT images can help hugely in the assessment of lung diseases. Many efforts have been made to develop airway segmentation algorithms, but methods are usually not optimized to be reliable across different CT scan parameters. Methods: In this paper, we present a simple and reliable semi-automatic algorithm which can segment tracheal and bronchial anatomy using the open-source 3D Slicer platform. The method is based on a region growing approach where trachea, right and left bronchi are cropped and segmented independently using three different thresholds. The algorithm and its parameters have been optimized to be efficient across different CT scan acquisition parameters. The performance of the proposed method has been evaluated on EXACT'09 cases and local clinical cases as well as on a breathing pig lung phantom using multiple scans and changing parameters. In particular, to investigate multiple scan parameters reconstruction kernel, radiation dose and slice thickness have been considered. Volume, branch count, branch length and leakage presence have been evaluated. A new method for leakage evaluation has been developed and correlation between segmentation metrics and CT acquisition parameters has been considered. Results: All the considered cases have been segmented successfully with good results in terms of leakage presence. Results on clinical data are comparable to other teams' methods, as obtained by evaluation against the EXACT09 challenge, whereas results obtained from the phantom prove the reliability of the method across multiple CT platforms and acquisition parameters. As expected, slice thickness is the parameter affecting the results the most, whereas reconstruction kernel and radiation dose seem not to particularly affect airway segmentation. Conclusion: The system represents the first open-source airway segmentation platform. The quantitative evaluation approach presented represents the first repeatable system evaluation tool for like-for-like comparison between different airway segmentation platforms. Results suggest that the algorithm can be considered stable across multiple CT platforms and acquisition parameters and can be considered as a starting point for the development of a complete airway segmentation algorithm. [ABSTRACT FROM AUTHOR]

Highlights: [•] Automatic segmentation of lung anatomical structures: airways, lung vessels and lung lobes. [•] Combination of segmented lung lobes and diffuse lung disease classification. [•] Airway segmentation by failure tracking and recovery algorithm. [ABSTRACT FROM AUTHOR]

A highly automated method for the segmentation of airways in the serial block-face cryomicrotome images of rat lungs is presented. First, a point inside of the trachea is manually specified. Then, a set of candidate airway centerline points is automatically identified. By utilizing a novel path extraction method, a centerline path between the root of the airway tree and each point in the set of candidate centerline points is obtained. Local disturbances are robustly handled by a novel path extraction approach, which avoids the shortcut problem of standard minimum cost path algorithms. The union of all centerline paths is utilized to generate an initial airway tree structure, and a pruning algorithm is applied to automatically remove erroneous subtrees or branches. Finally, a surface segmentation method is used to obtain the airway lumen. The method was validated on five image volumes of Sprague–Dawley rats. Based on an expert-generated independent standard, an assessment of airway identification and lumen segmentation performance was conducted. The average of airway detection sensitivity was 87.4% with a 95% confidence interval (CI) of (84.9, 88.6)%. A plot of sensitivity as a function of airway radius is provided. The combined estimate of airway detection specificity was 100% with a 95% CI of (99.4, 100)%. The average number and diameter of terminal airway branches was 1179 and 159 \mu \m, respectively. Segmentation results include airways up to 31 generations. The regression intercept and slope of airway radius measurements derived from final segmentations were estimated to be 7.22 \mum and 1.005, respectively. The developed approach enables the quantitative studies of physiology and lung diseases in rats, requiring detailed geometric airway models. [ABSTRACT FROM PUBLISHER]

• A coarse-to-fine segmentation framework to obtain a complete airway tree is proposed. • Location and boundary information are integrated into CNN to improv airway segmentation. • We combine CNN and region growing method to segment small airway branches. • Our method was validated on two datasets: private dataset, and an independent set of the EXACT'09 challenge. Background and Objectives: Automatic airway segmentation from chest computed tomography (CT) scans plays an important role in pulmonary disease diagnosis and computer-assisted therapy. However, low contrast at peripheral branches and complex tree-like structures remain as two mainly challenges for airway segmentation. Recent research has illustrated that deep learning methods perform well in segmentation tasks. Motivated by these works, a coarse-to-fine segmentation framework is proposed to obtain a complete airway tree. Methods: Our framework segments the overall airway and small branches via the multi-information fusion convolution neural network (Mif-CNN) and the CNN-based region growing, respectively. In Mif-CNN, atrous spatial pyramid pooling (ASPP) is integrated into a u-shaped network, and it can expend the receptive field and capture multi-scale information. Meanwhile, boundary and location information are incorporated into semantic information. These information are fused to help Mif-CNN utilize additional context knowledge and useful features. To improve the performance of the segmentation result, the CNN-based region growing method is designed to focus on obtaining small branches. A voxel classification network (VCN), which can entirely capture the rich information around each voxel, is applied to classify the voxels into airway and non-airway. In addition, a shape reconstruction method is used to refine the airway tree. Results: We evaluate our method on a private dataset and a public dataset from EXACT09. Compared with the segmentation results from other methods, our method demonstrated promising accuracy in complete airway tree segmentation. In the private dataset, the Dice similarity coefficient (DSC), Intersection over Union (IoU), false positive rate (FPR), and sensitivity are 93.5%, 87.8%, 0.015%, and 90.8%, respectively. In the public dataset, the DSC, IoU, FPR, and sensitivity are 95.8%, 91.9%, 0.053% and 96.6%, respectively. Conclusion: The proposed Mif-CNN and CNN-based region growing method segment the airway tree accurately and efficiently in CT scans. Experimental results also demonstrate that the framework is ready for application in computer-aided diagnosis systems for lung disease and other related works. [ABSTRACT FROM AUTHOR]