Robust 3-D airway tree segmentation for image-guided peripheral bronchoscopy

Label refinement network from synthetic error augmentation for medical image segmentation

Deep convolutional neural networks for image segmentation do not learn the label structure explicitly and may produce segmentations with an incorrect structure, e.g., with disconnected cylindrical structures in the segmentation of tree-like structures such as airways or blood vessels. In this paper, we propose a novel label refinement method to correct such errors from an initial segmentation, implicitly incorporating information about label structure. This method features two novel parts: (1) a model that generates synthetic structural errors, and (2) a label appearance simulation network that produces segmentations with synthetic errors that are similar in appearance to the real initial segmentations. Using these segmentations with synthetic errors and the original images, the label refinement network is trained to correct errors and improve the initial segmentations. The proposed method is validated on two segmentation tasks: airway segmentation from chest computed tomography (CT) scans and brain vessel segmentation from 3D CT angiography (CTA) images of the brain. In both applications, our method significantly outperformed a standard 3D U-Net, four previous label refinement methods, and a U-Net trained with a loss tailored for tubular structures. Improvements are even larger when additional unlabeled data is used for model training. In an ablation study, we demonstrate the value of the different components of the proposed method.

Detail-sensitive 3D-UNet for pulmonary airway segmentation from CT images

The segmentation of airway from computed tomography (CT) images plays a vital role in pulmonary disease diagnosis, evaluation, surgical planning, and treatment. Nevertheless, it is still challenging for current methods to handle distal thin and low-contrast airways, leading to mis-segmentation issues. This paper proposes a detail-sensitive 3D-UNet (DS-3D-UNet) that incorporates two new modules into 3D-UNet to segment airways accurately from CT images. The feature recalibration module is designed to give more attention to the foreground airway features through a new attention mechanism. The detail extractor module aims to restore multi-scale detailed features by fusion of features at different levels. Extensive experiments were conducted on the ATM'22 challenge dataset composed of 300 CT scans with airway annotations to evaluate its performance. Quantitative comparisons prove that the proposed model achieves the best performance in terms of Dice similarity coefficient (92.6%) and Intersection over Union (86.3%), outperforming other state-of-the-art methods. Qualitative comparisons further exhibit the superior performance of our method in segmenting thin and confused distal bronchi. The proposed model could provide important references for the diagnosis and treatment of pulmonary diseases, holding promising prospects in the field of digital medicine. Codes are available at https://github.com/nighlevil/DS-3D-UNet/tree/master .

Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge

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.

Integrating local and distant radiation-induced lung injury: Development and validation of a predictive model for ventilation loss

BACKGROUND

Investigations on radiation-induced lung injury (RILI) have predominantly focused on local effects, primarily those associated with radiation damage to lung parenchyma. However, recent studies from our group and others have revealed that radiation-induced damage to branching serial structures such as airways and vessels may also have a substantial impact on post-radiotherapy (RT) lung function. Furthermore, recent results from multiple functional lung avoidance RT trials, although promising, have demonstrated only modest toxicity reduction, likely because they were primarily focused on dose avoidance to lung parenchyma. These observations emphasize the critical need for predictive dose-response models that effectively incorporate both local and distant RILI effects.

PURPOSE

We develop and validate a predictive model for ventilation loss after lung RT. This model, referred to as P+A, integrates local (parenchyma [P]) and distant (central and peripheral airways [A]) radiation-induced damage, modeling partial (narrowing) and complete (collapse) obstruction of airways.

METHODS

In an IRB-approved prospective study, pre-RT breath-hold CTs (BHCTs) and pre- and one-year post-RT 4DCTs were acquired from lung cancer patients treated with definitive RT. Up to 13 generations of airways were automatically segmented on the BHCTs using a research virtual bronchoscopy software. Ventilation maps derived from the 4DCT scans were utilized to quantify pre- and post-RT ventilation, serving, respectively, as input data and reference standard (RS) in model validation. To predict ventilation loss solely due to parenchymal damage (referred to as P model), we used a normal tissue complication probability (NTCP) model. Our model used this NTCP-based estimate and predicted additional loss due radiation-induced partial or complete occlusion of individual airways, applying fluid dynamics principles and a refined version of our previously developed airway radiosensitivity model. Predictions of post-RT ventilation were estimated in the sublobar volumes (SLVs) connected to the terminal airways. To validate the model, we conducted a k-fold cross-validation. Model parameters were optimized as the values that provided the lowest root mean square error (RMSE) between predicted post-RT ventilation and the RS for all SLVs in the training data. The performance of the P+A and the P models was evaluated by comparing their respective post-RT ventilation values with the RS predictions. Additional evaluation using various receiver operating characteristic (ROC) metrics was also performed.

RESULTS

We extracted a dataset of 560 SLVs from four enrolled patients. Our results demonstrated that the P+A model consistently outperformed the P model, exhibiting RMSEs that were nearly half as low across all patients (13 ± 3 percentile for the P+A model vs. 24 ± 3 percentile for the P model on average). Notably, the P+A model aligned closely with the RS in ventilation loss distributions per lobe, particularly in regions exposed to doses ≥13.5 Gy. The ROC analysis further supported the superior performance of the P+A model compared to the P model in sensitivity (0.98 vs. 0.07), accuracy (0.87 vs. 0.25), and balanced predictions.

CONCLUSIONS

These early findings indicate that airway damage is a crucial factor in RILI that should be included in dose-response modeling to enhance predictions of post-RT lung function.

A Precise Pulmonary Airway Tree Segmentation Method Using Quasi-Spherical Region Constraint and Tracheal Wall Gap Sealing

Accurate segmentation of the pulmonary airway tree is crucial for diagnosing lung diseases. To tackle the issues of low segmentation accuracy and frequent leaks in existing methods, this paper proposes a precise segmentation method using quasi-spherical region-constrained wavefront propagation with tracheal wall gap sealing. Based on the characteristic that the surface formed by seed points approximates the airway cross-section, the width of the unsegmented airway is calculated, determining the initial quasi-spherical constraint region. Using the wavefront propagation method, seed points are continuously propagated and segmented along the tracheal wall within the quasi-spherical constraint region, thus overcoming the need to determine complex segmentation directions. To seal tracheal wall gaps, a morphological closing operation is utilized to extract the characteristics of small holes and locate low-brightness tracheal wall gaps. By filling the CT values at these gaps, the method seals the tracheal wall gaps. Extensive experiments on the EXACT09 dataset demonstrate that our algorithm ranks third in segmentation completeness. Moreover, its performance in preventing airway leaks is significantly better than the top-two algorithms, effectively preventing large-scale leak-induced spread.

Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation

Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases, while its manual delineation is unduly burdensome. To alleviate this time-consuming and potentially subjective manual procedure, researchers have proposed methods to automatically segment airways from computerized tomography (CT) images. However, some small-sized airway branches (e.g., bronchus and terminal bronchioles) significantly aggravate the difficulty of automatic segmentation by machine learning models. In particular, the variance of voxel values and the severe data imbalance in airway branches make the computational module prone to discontinuous and false-negative predictions, especially for cohorts with different lung diseases. The attention mechanism has shown the capacity to segment complex structures, while fuzzy logic can reduce the uncertainty in feature representations. Therefore, the integration of deep attention networks and fuzzy theory, given by the fuzzy attention layer, should be an escalated solution for better generalization and robustness. This article presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network (FANN) and a comprehensive loss function to enhance the spatial continuity of airway segmentation. The deep fuzzy set is formulated by a set of voxels in the feature map and a learnable Gaussian membership function. Different from the existing attention mechanism, the proposed channel-specific fuzzy attention addresses the issue of heterogeneous features in different channels. Furthermore, a novel evaluation metric is proposed to assess both the continuity and completeness of airway structures. The efficiency, generalization, and robustness of the proposed method have been proved by training on normal lung disease while testing on datasets of lung cancer, COVID-19, and pulmonary fibrosis.

Tree Recovery by Dynamic Programming

Tree-like structures are common, naturally occurring objects that are of interest to many fields of study, such as plant science and biomedicine. Analysis of these structures is typically based on skeletons extracted from captured data, which often contain spurious cycles that need to be removed. We propose a dynamic programming algorithm for solving the NP-hard tree recovery problem formulated by (Estrada et al. 2015), which seeks a least-cost partitioning of the graph nodes that yields a directed tree. Our algorithm finds the optimal solution by iteratively contracting the graph via node-merging until the problem can be trivially solved. By carefully designing the merging sequence, our algorithm can efficiently recover optimal trees for many real-world data where (Estrada et al. 2015) only produces sub-optimal solutions. We also propose an approximate variant of dynamic programming using beam search, which can process graphs containing thousands of cycles with significantly improved optimality and efficiency compared with (Estrada et al. 2015).

A Survey on Artificial Intelligence in Pulmonary Imaging

Over the last decade, deep learning (DL) has contributed a paradigm shift in computer vision and image recognition creating widespread opportunities of using artificial intelligence in research as well as industrial applications. DL has been extensively studied in medical imaging applications, including those related to pulmonary diseases. Chronic obstructive pulmonary disease, asthma, lung cancer, pneumonia, and, more recently, COVID-19 are common lung diseases affecting nearly 7.4% of world population. Pulmonary imaging has been widely investigated toward improving our understanding of disease etiologies and early diagnosis and assessment of disease progression and clinical outcomes. DL has been broadly applied to solve various pulmonary image processing challenges including classification, recognition, registration, and segmentation. This paper presents a survey of pulmonary diseases, roles of imaging in translational and clinical pulmonary research, and applications of different DL architectures and methods in pulmonary imaging with emphasis on DL-based segmentation of major pulmonary anatomies such as lung volumes, lung lobes, pulmonary vessels, and airways as well as thoracic musculoskeletal anatomies related to pulmonary diseases.

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

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

Efficient procedure planning for comprehensive lymph node staging bronchoscopy

Purpose: For a patient at risk of having lung cancer, accurate disease staging is vital as it dictates disease prognosis and treatment. Accurate staging requires a comprehensive sampling of lymph nodes within the chest via bronchoscopy. Unfortunately, physicians are generally unable to plan and perform sufficiently comprehensive procedures to ensure accurate disease staging. We propose a method for planning comprehensive lymph node staging procedures. Approach: Drawing on a patient's chest CT scan, the method derives a multi-destination tour for efficient navigation to a set of lymph nodes. We formulate the planning task as a traveling salesman problem. To solve the problem, we apply the concept of ant colony optimization (ACO) to derive an efficient airway tour connecting the target nodes. The method has three main steps: (1) CT preprocessing, to define important chest anatomy; (2) graph and staging zone construction, to set up the necessary data structures and clinical constraints; and (3) tour computation, to derive the staging plan. The plan conforms to the world standard International Association for the Study of Lung Cancer (IASLC) lymph node map and recommended clinical staging guidelines. Results: Tests with a patient database indicate that the method derives optimal or near-optimal tours in under a few seconds, regardless of the number of target lymph nodes (mean tour length = 1.4% longer than the optimum). A brute force optimal search, on the other hand, generally cannot reach a solution in under 10 min. for patients exhibiting > 16 nodes, and other methods provide poor solutions. We also demonstrate the method's utility in an image-guided bronchoscopy system. Conclusions: The method provides an efficient computational approach for planning a comprehensive lymph node staging bronchoscopy. In addition, the method shows promise for driving an image-guided bronchoscopy system or robotics-assisted bronchoscopy system tailored to lymph node staging.

Multimodal Registration for Image-Guided EBUS Bronchoscopy

The state-of-the-art procedure for examining the lymph nodes in a lung cancer patient involves using an endobronchial ultrasound (EBUS) bronchoscope. The EBUS bronchoscope integrates two modalities into one device: (1) videobronchoscopy, which gives video images of the airway walls; and (2) convex-probe EBUS, which gives 2D fan-shaped views of extraluminal structures situated outside the airways. During the procedure, the physician first employs videobronchoscopy to navigate the device through the airways. Next, upon reaching a given node's approximate vicinity, the physician probes the airway walls using EBUS to localize the node. Due to the fact that lymph nodes lie beyond the airways, EBUS is essential for confirming a node's location. Unfortunately, it is well-documented that EBUS is difficult to use. In addition, while new image-guided bronchoscopy systems provide effective guidance for videobronchoscopic navigation, they offer no assistance for guiding EBUS localization. We propose a method for registering a patient's chest CT scan to live surgical EBUS views, thereby facilitating accurate image-guided EBUS bronchoscopy. The method entails an optimization process that registers CT-based virtual EBUS views to live EBUS probe views. Results using lung cancer patient data show that the method correctly registered 28/28 (100%) lymph nodes scanned by EBUS, with a mean registration time of 3.4 s. In addition, the mean position and direction errors of registered sites were 2.2 mm and 11.8∘, respectively. In addition, sensitivity studies show the method's robustness to parameter variations. Lastly, we demonstrate the method's use in an image-guided system designed for guiding both phases of EBUS bronchoscopy.

Combining Serial and Parallel Functionality in Functional Lung Avoidance Radiation Therapy

PURPOSE

Functional lung avoidance (FLA) radiation therapy (RT) aims to minimize post-RT pulmonary toxicity by preferentially avoiding dose to high-functioning lung (HFL) regions. A common limitation is that FLA approaches do not consider the conducting architecture for gas exchange. We previously proposed the functionally weighted airway sparing (FWAS) method to spare airways connected to HFL regions, showing that it is possible to substantially reduce risk of radiation-induced airway injury. Here, we compare the performance of FLA and FWAS and propose a novel method combining both approaches.

METHODS

We used breath-hold computed tomography (BHCT) and simulation 4-dimensional computed tomography (4DCT) from 12 lung stereotactic ablative radiation therapy patients. Four planning strategies were examined: (1) Conventional: no sparing other than clinical dose-volume constraints; (2) FLA: using a 4DCT-based ventilation map to delineate the HFL, plans were optimized to reduce mean dose and V_13.50 in HFL; (3) FWAS: we autosegemented 11 to 13 generations of individual airways from each patient's BHCT and assigned priorities based on the relative contribution of each airway to total ventilation. We used these priorities in the optimization along with airway dose constraints, estimated as a function of airway diameter and 5% probability of collapse; and (4) FLA + FWAS: we combined information from the 2 strategies. We prioritized clinical dose constraints for organs at risk and planning target volume in all plans. We performed the evaluation in terms of ventilation preservation accounting for radiation-induced damage to both lung parenchyma and airways.

RESULTS

We observed average ventilation preservation for FLA, FWAS, and FLA + FWAS as 3%, 8.5%, and 14.5% higher, respectively, than for Conventional plans for patients with ventilation preservation in Conventional plans <90%. Generalized estimated equations showed that all improvements were statistically significant (P ≤ .036). We observed no clinically relevant improvements in outcomes of the sparing techniques in patients with ventilation preservation in Conventional plans ≥90%.

CONCLUSIONS

These initial results suggest that it is crucial to consider the parallel and the serial nature of the lung to improve post-radiation therapy lung function and, consequently, quality of life for patients.

Visually Navigated Bronchoscopy using three cycle-Consistent generative adversarial network for depth estimation

[Background] Electromagnetically Navigated Bronchoscopy (ENB) is currently the state-of-the art diagnostic and interventional bronchoscopy. CT-to-body divergence is a critical hurdle in ENB, causing navigation error and ultimately limiting the clinical efficacy of diagnosis and treatment. In this study, Visually Navigated Bronchoscopy (VNB) is proposed to address the aforementioned issue of CT-to-body divergence. [Materials and Methods] We extended and validated an unsupervised learning method to generate a depth map directly from bronchoscopic images using a Three Cycle-Consistent Generative Adversarial Network (3cGAN) and registering the depth map to preprocedural CTs. We tested the working hypothesis that the proposed VNB can be integrated to the navigated bronchoscopic system based on 3D Slicer, and accurately register bronchoscopic images to pre-procedural CTs to navigate transbronchial biopsies. The quantitative metrics to asses the hypothesis we set was Absolute Tracking Error (ATE) of the tracking and the Target Registration Error (TRE) of the total navigation system. We validated our method on phantoms produced from the pre-procedural CTs of five patients who underwent ENB and on two ex-vivo pig lung specimens. [Results] The ATE using 3cGAN was 6.2 +/- 2.9 [mm]. The ATE of 3cGAN was statistically significantly lower than that of cGAN, particularly in the trachea and lobar bronchus (p < 0.001). The TRE of the proposed method had a range of 11.7 to 40.5 [mm]. The TRE computed by 3cGAN was statistically significantly smaller than those computed by cGAN in two of the five cases enrolled (p < 0.05). [Conclusion] VNB, using 3cGAN to generate the depth maps was technically and clinically feasible. While the accuracy of tracking by cGAN was acceptable, the TRE warrants further investigation and improvement.

Image-guided EBUS bronchoscopy system for lung-cancer staging

The staging of the central-chest lymph nodes is a major step in the management of lung-cancer patients. For this purpose, the physician uses a device that integrates videobronchoscopy and an endobronchial ultrasound (EBUS) probe. To biopsy a lymph node, the physician first uses videobronchoscopy to navigate through the airways and then invokes EBUS to localize and biopsy the node. Unfortunately, this process proves difficult for many physicians, with the choice of biopsy site found by trial and error. We present a complete image-guided EBUS bronchoscopy system tailored to lymph-node staging. The system accepts a patient’s 3D chest CT scan, an optional PET scan, and the EBUS bronchoscope’s video sources as inputs. System workflow follows two phases: (1) procedure planning and (2) image-guided EBUS bronchoscopy. Procedure planning derives airway guidance routes that facilitate optimal EBUS scanning and nodal biopsy. During the live procedure, the system’s graphical display suggests a series of device maneuvers to perform and provides multimodal visual cues for locating suitable biopsy sites. To this end, the system exploits data fusion to drive a multimodal virtual bronchoscope and other visualization tools that lead the physician through the process of device navigation and localization. A retrospective lung-cancer patient study and follow-on prospective patient study, performed within the standard clinical workflow, demonstrate the system’s feasibility and functionality. For the prospective study, 60/60 selected lymph nodes (100%) were correctly localized using the system, and 30/33 biopsied nodes (91%) gave adequate tissue samples. Also, the mean procedure time including all user interactions was 6 min 43 s All of these measures improve upon benchmarks reported for other state-of-the-art systems and current practice. Overall, the system enabled safe, efficient EBUS-based localization and biopsy of lymph nodes.

Virtual bronchoscopy-guided lung SAbR: dosimetric implications of using AAA versus Acuros XB to calculate dose in airways

In previous works, we showed that incorporating individual airways as organs-at-risk (OARs) in the treatment of lung stereotactic ablative radiotherapy (SAbR) patients potentially mitigates post-SAbR radiation injury. However, the performance of common clinical dose calculation algorithms in airways has not been thoroughly studied. Airways are of particular concern because their small size and the density differences they create have the potential to hinder dose calculation accuracy. To address this gap in knowledge, here we investigate dosimetric accuracy in airways of two commonly used dose calculation algorithms, the anisotropic analytical algorithm (AAA) and Acuros-XB (AXB), recreating clinical treatment plans on a cohort of four SAbR patients. A virtual bronchoscopy software was used to delineate 856 airways on a high-resolution breath-hold CT (BHCT) image acquired for each patient. The planning target volumes (PTVs) and standard thoracic OARs were contoured on an average CT (AVG) image over the breathing cycle. Conformal and intensity-modulated radiation therapy plans were recreated on the BHCT image and on the AVG image, for a total of four plan types per patient. Dose calculations were performed using AAA and AXB, and the differences in maximum and mean dose in each structure were calculated. The median differences in maximum dose among all airways were ≤0.3Gy in magnitude for all four plan types. With airways grouped by dose-to-structure or diameter, median dose differences were still ≤0.5Gy in magnitude, with no clear dependence on airway size. These results, along with our previous airway radiosensitivity works, suggest that dose differences between AAA and AXB correspond to an airway collapse variation ≤0.7% in magnitude. This variation in airway injury risk can be considered as not clinically relevant, and the use of either AAA or AXB is therefore appropriate when including patient airways as individual OARs so as to reduce risk of radiation-induced lung toxicity.

Automatic airway segmentation from computed tomography using robust and efficient 3-D convolutional neural networks

This paper presents a fully automatic and end-to-end optimised airway segmentation method for thoracic computed tomography, based on the U-Net architecture. We use a simple and low-memory 3D U-Net as backbone, which allows the method to process large 3D image patches, often comprising full lungs, in a single pass through the network. This makes the method simple, robust and efficient. We validated the proposed method on three datasets with very different characteristics and various airway abnormalities: (1) a dataset of pediatric patients including subjects with cystic fibrosis, (2) a subset of the Danish Lung Cancer Screening Trial, including subjects with chronic obstructive pulmonary disease, and (3) the EXACT'09 public dataset. We compared our method with other state-of-the-art airway segmentation methods, including relevant learning-based methods in the literature evaluated on the EXACT'09 data. We show that our method can extract highly complete airway trees with few false positive errors, on scans from both healthy and diseased subjects, and also that the method generalizes well across different datasets. On the EXACT'09 test set, our method achieved the second highest sensitivity score among all methods that reported good specificity.

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

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

Extraction of pulmonary Trachea by dynamic tubular edge contour algorithm

Background

One of the difficulties and hot topics in the field of computer vision and image processing is extraction of the high-level pulmonary trachea from patients’ lung CT images. Current, common bronchial extraction methods are limited by the phenomenon of bronchial loss and leakage, and cannot extract the higher-level pulmonary trachea, which does not meet the requirements of guiding lung puncture procedures.

Methods

Based on the characteristic “tubular structure” (ring or semi-closed ring) of the pulmonary trachea in CT images, an algorithm based on dynamic tubular edge contour is proposed. In axial, coronal and sagittal CT images, the algorithm could extract the skeletal line of the pulmonary trachea and vessel-connecting region, perform elliptical fitting, extract the pulmonary trachea by the ratio of the ellipse’s long and short axes, and obtain point cloud data of the pulmonary trachea in three directions. The point cloud data was fused to obtain a complete three-dimensional model of the pulmonary trachea.

Results

The algorithm was verified using CT data from “EXACT09”, and could extract the pulmonary trachea to the 10–11 level, which effectively solves the problems of leakage and loss of the trachea.

Conclusions

We have constructed a novel extraction algorithm of pulmonary trachea that can guide the doctors to decide the puncture path and avoid the large trachea, which has important theoretical and practical significance for reducing puncture complications and the mortality rate.

Functionally weighted airway sparing (FWAS): a functional avoidance method for preserving post-treatment ventilation in lung radiotherapy

Recent changes to the guidelines for screening and early diagnosis of lung cancer have increased the interest in preserving post-radiotherapy lung function. Current investigational approaches are based on spatially mapping functional regions and generating regional avoidance plans that preferentially spare highly ventilated/perfused lung. A potentially critical, yet overlooked, aspect of functional avoidance is radiation injury to peripheral airways, which serve as gas conduits to and from functional lung regions. Dose redistribution based solely on regional function may cause irreparable damage to the 'supply chain'. To address this deficiency, we propose the functionally weighted airway sparing (FWAS) method. FWAS (i) maps the bronchial pathways to each functional sub-lobar lung volume; (ii) assigns a weighting factor to each airway based on the relative contribution of the sub-volume to overall lung function; and (iii) creates a treatment plan that aims to preserve these functional pathways. To evaluate it, we used four cases from a retrospective cohort of SAbR patients treated for lung cancer. Each patient's airways were auto-segmented from a diagnostic-quality breath-hold CT using a research virtual bronchoscopy software. A ventilation map was generated from the planning 4DCT to map regional lung function. For each terminal airway, as resolved by the segmentation software, the total ventilation within the sub-lobar volume supported by that airway was estimated and used as a function-based weighting factor. Upstream airways were weighted based on the cumulative volumetric ventilation supported by corresponding downstream airways. Using a previously developed model for airway radiosensitivity, dose constraints were determined for each airway corresponding to a <5% probability of airway collapse. Airway dose constraints, ventilation scores, and clinical dose constraints were input to a swarm optimization-based inverse planning engine to create a 3D conformal SAbR plan (CRT). The FWAS plans were compared to the patients' prescribed CRT clinical plans and the inverse-optimized clinical plans. Depending on the size and location of the tumour, the FWAS plan showed superior preservation of ventilation due to airflow preservation through open pathways (i.e. cumulative ventilation score from the sub-lobar volumes of open pathways). Improvements ranged between 3% and 23%, when comparing to the prescribed clinical plans, and between 3% and 35%, when comparing to the inverse-optimized clinical plans. The three plans satisfied clinical requirements for PTV coverage and OAR dose constraints. These initial results suggest that by sparing pathways to high-functioning lung subregions it is possible to reduce post-SAbR loss of respiratory function.

Region growing algorithm combined with morphology and skeleton analysis for segmenting airway tree in CT images

BACKGROUND

Automatic segmentation of pulmonary airway tree is a challenging task in many clinical applications, including developing computer-aided detection and diagnosis schemes of lung diseases.

OBJECTIVE

To segment the pulmonary airway tree from the computed tomography (CT) chest images using a novel automatic method proposed in this study.

METHODS

This method combines a two-pass region growing algorithm with gray-scale morphological reconstruction and leakage elimination. The first-pass region growing is implemented to obtain a rough airway tree. The second-pass region growing and gray-scale morphological reconstruction are used to detect the distal airways. Finally, leakage detection is performed to remove leakage and refine the airway tree.

RESULTS

Our methods were compared with the gold standards. Forty-five clinical CT lung image scan cases were used in the experiments. Statistics on tree division order, branch number, and airway length were adopted for evaluation. The proposed method detected up to 12 generations of bronchi. On average, 148.85 branches were extracted with a false positive rate of 0.75%.

CONCLUSIONS

The results show that our method is accurate for pulmonary airway tree segmentation. The strategy of separating the leakage detection from the segmenting process is feasible and promising for ensuring a high branch detected rate with a low leakage volume.

Segmentation of distal airways using structural analysis

Segmentation of airways in Computed Tomography (CT) scans is a must for accurate support of diagnosis and intervention of many pulmonary disorders. In particular, lung cancer diagnosis would benefit from segmentations reaching most distal airways. We present a method that combines descriptors of bronchi local appearance and graph global structural analysis to fine-tune thresholds on the descriptors adapted for each bronchial level. We have compared our method to the top performers of the EXACT09 challenge and to a commercial software for biopsy planning evaluated in an own-collected data-base of high resolution CT scans acquired under different breathing conditions. Results on EXACT09 data show that our method provides a high leakage reduction with minimum loss in airway detection. Results on our data-base show the reliability across varying breathing conditions and a competitive performance for biopsy planning compared to a commercial solution.

Accounting for respiratory motion in small serial structures during radiotherapy planning: proof of concept in virtual bronchoscopy-guided lung functional avoidance radiotherapy

Respiratory motion management techniques in radiotherapy (RT) planning are primarily focused on maintaining tumor target coverage. An inadequately addressed need is accounting for motion in dosimetric estimations in smaller serial structures. Accurate dose estimations in such structures are more sensitive to motion because respiration can cause them to move completely in or out of a high dose-gradient field. In this work, we study three motion management strategies (m1-m3) to find an accurate method to estimate the dosimetry in airways. To validate these methods, we generated a 'ground truth' digital breathing model based on a 4DCT scan from a lung stereotactic ablative radiotherapy (SAbR) patient. We simulated 225 breathing cycles with  ±10% perturbations in amplitude, respiratory period, and time per respiratory phase. A high-resolution breath-hold CT (BHCT) was also acquired and used with a research virtual bronchoscopy software to autosegment 239 airways. Contours for planning target volume (PTV) and organs at risk (OARs) were defined on the maximum intensity projection of the 4DCT (CT_MIP) and transferred to the average of the 10 4DCT phases (CT_AVG). To design the motion management methods, the RT plan was recreated using different images and structure definitions. Methods m1 and m2 recreated the plan using the CT_AVG image. In method m1, airways were deformed to the CT_AVG. In m2, airways were deformed to each of the 4DCT phases, and union structures were transferred onto the CT_AVG. In m3, the RT plan was recreated on each of the 10 phases, and the dose distribution from each phase was deformed to the BHCT and summed. Dose errors (mean [min, max]) in airways were: m1: 21% (0.001%, 93%); m2: 45% (0.1%, 179%); and m3: 4% (0.006%, 14%). Our work suggests that accurate dose estimation in moving small serial structures requires customized motion management techniques (like m3 in this work) rather than current clinical and investigational approaches.

Optimal route planning for image-guided EBUS bronchoscopy

The staging of the central-chest lymph nodes is a major lung-cancer management procedure. To perform a staging procedure, the physician first uses a patient's 3D X-ray computed-tomography (CT) chest scan to interactively plan airway routes leading to selected target lymph nodes. Next, using an integrated EBUS bronchoscope (EBUS = endobronchial ultrasound), the physician uses videobronchoscopy to navigate through the airways toward a target node's general vicinity and then invokes EBUS to localize the node for biopsy. Unfortunately, during the procedure, the physician has difficulty in translating the preplanned airway routes into safe, effective biopsy sites. We propose an automatic route-planning method for EBUS bronchoscopy that gives optimal localization of safe, effective nodal biopsy sites. To run the method, a 3D chest model is first computed from a patient's chest CT scan. Next, an optimization method derives feasible airway routes that enables maximal tissue sampling of target lymph nodes while safely avoiding major blood vessels. In a lung-cancer patient study entailing 31 nodes (long axis range: [9.0 mm, 44.5 mm]), 25/31 nodes yielded safe airway routes having an optimal tissue sample size = 8.4 mm (range: [1.0 mm, 18.6 mm]) and sample adequacy = 0.42 (range: [0.05, 0.93]). Quantitative results indicate that the method potentially enables successful biopsies in essentially 100% of selected lymph nodes versus the 70-94% success rate of other approaches. The method also potentially facilitates adequate tissue biopsies for nearly 100% of selected nodes, as opposed to the 55-77% tissue adequacy rates of standard methods. The remaining nodes did not yield a safe route within the preset safety-margin constraints, with 3 nodes never yielding a route even under the most lenient safety-margin conditions. Thus, the method not only helps determine effective airway routes and expected sample quality for nodal biopsy, but it also helps point out situations where biopsy may not be advisable. We also demonstrate the methodology in an image-guided EBUS bronchoscopy system, used successfully in live lung-cancer patient studies. During a live procedure, the method provides dynamic real-time sample size visualization in an enhanced virtual bronchoscopy viewer. In this way, the physician vividly sees the most promising biopsy sites along the airway walls as the bronchoscope moves through the airways.

Improvement of fully automated airway segmentation on volumetric computed tomographic images using a 2.5 dimensional convolutional neural net

We propose a novel airway segmentation method in volumetric chest computed tomography (CT) and evaluate its performance on multiple datasets. The segmentation is performed voxel-by-voxel by a 2.5D convolutional neural net (2.5D CNN) trained in a supervised manner. To enhance the accuracy of the segmented airway tree, we simultaneously took three adjacent slices in each of the orthogonal directions including axial, sagittal, and coronal and fine-tuned the parameters that influence the tree length and the number of leakage. The gold standard of airway segmentation was generated by a semi-automated method using AVIEW™. The 2.5D CNN was trained and evaluated on a subset of inspiratory thoracic CT scans taken from the Korean obstructive lung disease study, which includes normal subjects and chronic obstructive pulmonary disease patients. The reliability and further practicality of our proposed method was demonstrated in multiple datasets. In eight test datasets collected by the same imaging protocol, the percentage detected tree length, false positive rate, and Dice similarity coefficient of our method were 92.16%, 7.74%, and 0.8997 ± 0.0892, respectively. In 20 test datasets of the EXACT'09 challenge, the percentage detected tree length was 60.1% and the false positive rate was 4.56%. Our fully automated (end-to-end) segmentation method could be applied in radiologic practice.

Efficient Bronchoscopic Video Summarization

Bronchoscopy enables many minimally invasive chest procedures for diseases such as lung cancer and asthma. Guided by the bronchoscope's video stream, a physician can navigate the complex three-dimensional (3-D) airway tree to collect tissue samples or administer a disease treatment. Unfortunately, physicians currently discard procedural video because of the overwhelming amount of data generated. Hence, they must rely on memory and anecdotal snapshots to document a procedure. We propose a robust automatic method for summarizing an endobronchial video stream. Inspired by the multimedia concept of the video summary and by research in other endoscopy domains, our method consists of three main steps: 1) shot segmentation, 2) motion analysis, and 3) keyframe selection. Overall, the method derives a true hierarchical decomposition, consisting of a shot set and constituent keyframe set, for a given procedural video. No other method to our knowledge gives such a structured summary for the raw, unscripted, unedited videos arising in endoscopy. Results show that our method more efficiently covers the observed endobronchial regions than other keyframe-selection approaches and is robust to parameter variations. Over a wide range of video sequences, our method required on average only 6.5% of available video frames to achieve a video coverage = 92.7%. We also demonstrate how the derived video summary facilitates direct fusion with a patient's 3-D chest computed-tomography scan in a system under development, thereby enabling efficient video browsing and retrieval through the complex airway tree.

Virtual Bronchoscopy-Guided Treatment Planning to Map and Mitigate Radiation-Induced Airway Injury in Lung SAbR

PURPOSE

Radiation injury to the bronchial tree is an important yet poorly understood potential side effect in lung stereotactic ablative radiation therapy (SAbR). We investigate the integration of virtual bronchoscopy in radiation therapy planning to quantify dosage to individual airways. We develop a risk model of airway collapse and develop treatment plans that reduce the risk of radiation-induced airway injury.

METHODS AND MATERIALS

Pre- and post-SAbR diagnostic-quality computerized tomography (CT) scans were retrospectively collected from 26 lung cancer patients. From each scan, the bronchial tree was segmented using a virtual bronchoscopy system and registered deformably to the planning CT. Univariate and stepwise multivariate Cox regressions were performed, examining factors such as age, comorbidities, smoking pack years, airway diameter, and maximum point dosage (D_max). Logistic regression was utilized to formulate a risk function of segmental collapse based on D_max and diameter. The risk function was incorporated into the objective function along with clinical dosage volume constraints for planning target volume (PTV) and organs at risk (OARs).

RESULTS

Univariate analysis showed that segmental diameter (P = .014) and D_max (P = .007) were significantly correlated with airway segment collapse. Multivariate stepwise Cox regression showed that diameter (P = .015), D_max (P < .0001), and pack/years of smoking (P = .02) were significant independent factors associated with collapse. Risk management-based plans enabled significant dosage reduction to individual airway segments while fulfilling clinical dosimetric objectives.

CONCLUSION

To our knowledge, this is the first systematic investigation of functional avoidance in lung SAbR based on mapping and minimizing doses to individual bronchial segments. Our early results show that it is possible to substantially lower airway dosage. Such dosage reduction may potentially reduce the risk of radiation-induced airway injury, while satisfying clinically prescribed dosimetric objectives.

Airway tree reconstruction in expiration chest CT scans facilitated by information transfer from corresponding inspiration scans

PURPOSE

Analysis and comparison of airways imaged in pairs of inspiration and expiration lung CT scans provides important information for quantitative assessment of lung diseases like chronic obstructive pulmonary disease. However, airway tree reconstruction in expiration CT scans is a challenging problem. Typically, only a low number of airway branches are found in expiration scans, compared to inspiration scans. To detect more airways in expiration CT scans, the authors introduce a novel airway reconstruction approach and assess its performance.

METHODS

The method requires a pair of inspiration and expiration CT scans and utilizes information from the inspiration scan to facilitate reconstructing the airway tree in the expiration lung CT scan. First, an initial airway tree (high confidence) and airway candidates (limited confidence) are reconstructed in the expiration scan by utilizing a 3D graph-based reconstruction method. Then, the 3D airway tree is reconstructed in the inspiration scan. Second, correspondences between expiration and inspiration tree structures are identified by utilizing a novel hierarchical tree matching algorithm that utilizes a local CT image-based similarity criterion. Third, the tree information from the inspiration airway tree is used to select expiration candidates, resulting in the final expiration tree structure. The approach was evaluated on a diverse set of 40 scan pairs and compared to the baseline method, which utilizes only the expiration CT scan.

RESULTS

The proposed method produced a significant (p < 0.05) increase in airway tree length by 13.35 cm, on average, which represents an 11.21% increase relative to the baseline result using only the expiration CT scan. A detailed analysis of all additionally identified airways resulted in a true and false positive rate of 94.8% and 5.2%, respectively. The true positive rate was found to be significantly higher than the false positive rate (p < 0.05).

CONCLUSIONS

The proposed method allowed increasing the number of found airways in expiration scans significantly. In addition, the algorithm establishes correspondence between inspiration and expiration airway trees, which can facilitate label transfer between airway trees and quantitative assessment of change in airways. The approach can be adapted to facilitate airway reconstruction in several longitudinal lung CT scans by means of mutual information transfer.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Thoracic cavity definition for 3D PET/CT analysis and visualization

X-ray computed tomography (CT) and positron emission tomography (PET) serve as the standard imaging modalities for lung-cancer management. CT gives anatomical details on diagnostic regions of interest (ROIs), while PET gives highly specific functional information. During the lung-cancer management process, a patient receives a co-registered whole-body PET/CT scan pair and a dedicated high-resolution chest CT scan. With these data, multimodal PET/CT ROI information can be gleaned to facilitate disease management. Effective image segmentation of the thoracic cavity, however, is needed to focus attention on the central chest. We present an automatic method for thoracic cavity segmentation from 3D CT scans. We then demonstrate how the method facilitates 3D ROI localization and visualization in patient multimodal imaging studies. Our segmentation method draws upon digital topological and morphological operations, active-contour analysis, and key organ landmarks. Using a large patient database, the method showed high agreement to ground-truth regions, with a mean coverage=99.2% and leakage=0.52%. Furthermore, it enabled extremely fast computation. For PET/CT lesion analysis, the segmentation method reduced ROI search space by 97.7% for a whole-body scan, or nearly 3 times greater than that achieved by a lung mask. Despite this reduction, we achieved 100% true-positive ROI detection, while also reducing the false-positive (FP) detection rate by >5 times over that achieved with a lung mask. Finally, the method greatly improved PET/CT visualization by eliminating false PET-avid obscurations arising from the heart, bones, and liver. In particular, PET MIP views and fused PET/CT renderings depicted unprecedented clarity of the lesions and neighboring anatomical structures truly relevant to lung-cancer assessment.

Optimizing parameters of an open-source airway segmentation algorithm using different CT images

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.

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

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

Review of automatic pulmonary lobe segmentation methods from CT

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

Hands-Free System for Bronchoscopy Planning and Guidance

Bronchoscopy is a commonly used minimally invasive procedure for lung-cancer staging. In standard practice, however, physicians differ greatly in their levels of performance. To address this concern, image-guided intervention (IGI) systems have been devised to improve procedure success. Current IGI bronchoscopy systems based on virtual bronchoscopic navigation (VBN), however, require involvement from the attending technician. This lessens physician control and hinders the overall acceptance of such systems. We propose a hands-free VBN system for planning and guiding bronchoscopy. The system introduces two major contributions. First, it incorporates a new procedure-planning method that automatically computes airway navigation plans conforming to the physician's bronchoscopy training and manual dexterity. Second, it incorporates a guidance strategy for bronchoscope navigation that enables user-friendly system control via a foot switch, coupled with a novel position-verification mechanism. Phantom studies verified that the system enables smooth operation under physician control, while also enabling faster navigation than an existing technician-assisted VBN system. In a clinical human study, we noted a 97% bronchoscopy navigation success rate, in line with existing VBN systems, and a mean guidance time per diagnostic site = 52 s. This represents a guidance time often nearly 3 min faster per diagnostic site than guidance times reported for other technician-assisted VBN systems. Finally, an ergonomic study further asserts the system's acceptability to the physician and long-term potential.

Utility of three-dimensional computed tomography in general thoracic surgery

It is important for general thoracic surgeons to understand the relationship between tumors and surrounding organs during surgery; however, many anatomical variations are possible in the thorax, which can complicate this goal. Multidetector computed tomography (MDCT) is the latest technical breakthrough in CT imaging. MDCT permits rapid scanning of large areas of the body with multiple detectors, thereby allowing for simultaneous acquisition of an increased number of transaxial CT slices, which reduce motion artifacts. Three-dimensional (3D) rendering involves the creation of two-dimensional images that convey the 3D relationship of objects. The 3D reconstruction allows for enormous quantity of data to be utilized intuitively and effectively. The final images can reveal various lesions or organs of interest with high anatomical detail and accuracy to the general thoracic surgeon, which is helpful in performing safer surgeries. Surgeries for the following can benefit from this technology: lung lobectomy or segmentectomy, pulmonary sequestration, cardiovascular malformation, tracheobronchial tree, mediastinum, and chest wall. This article reviews the utility of 3D-MDCT imaging in the field of general thoracic surgery.

GPU accelerated segmentation and centerline extraction of tubular structures from medical images

PURPOSE

To create a fast and generic method with sufficient quality for extracting tubular structures such as blood vessels and airways from different modalities (CT, MR and US) and organs (brain, lungs and liver) by utilizing the computational power of graphic processing units (GPUs).

METHODS

A cropping algorithm is used to remove unnecessary data from the datasets on the GPU. A model-based tube detection filter combined with a new parallel centerline extraction algorithm and a parallelized region growing segmentation algorithm is used to extract the tubular structures completely on the GPU. Accuracy of the proposed GPU method and centerline algorithm is compared with the ridge traversal and skeletonization/thinning methods using synthetic vascular datasets.

RESULTS

The implementation is tested on several datasets from three different modalities: airways from CT, blood vessels from MR, and 3D Doppler Ultrasound. The results show that the method is able to extract airways and vessels in 3-5 s on a modern GPU and is less sensitive to noise than other centerline extraction methods.

CONCLUSIONS

Tubular structures such as blood vessels and airways can be extracted from various organs imaged by different modalities in a matter of seconds, even for large datasets.

Optimal procedure planning and guidance system for peripheral bronchoscopy

With the development of multidetector computed-tomography (MDCT) scanners and ultrathin bronchoscopes, the use of bronchoscopy for diagnosing peripheral lung-cancer nodules is becoming a viable option. The work flow for assessing lung cancer consists of two phases: 1) 3-D MDCT analysis and 2) live bronchoscopy. Unfortunately, the yield rates for peripheral bronchoscopy have been reported to be as low as 14%, and bronchoscopy performance varies considerably between physicians. Recently, proposed image-guided systems have shown promise for assisting with peripheral bronchoscopy. Yet, MDCT-based route planning to target sites has relied on tedious error-prone techniques. In addition, route planning tends not to incorporate known anatomical, device, and procedural constraints that impact a feasible route. Finally, existing systems do not effectively integrate MDCT-derived route information into the live guidance process. We propose a system that incorporates an automatic optimal route-planning method, which integrates known route constraints. Furthermore, our system offers a natural translation of the MDCT-based route plan into the live guidance strategy via MDCT/video data fusion. An image-based study demonstrates the route-planning method's functionality. Next, we present a prospective lung-cancer patient study in which our system achieved a successful navigation rate of 91% to target sites. Furthermore, when compared to a competing commercial system, our system enabled bronchoscopy over two airways deeper into the airway-tree periphery with a sample time that was nearly 2 min shorter on average. Finally, our system's ability to almost perfectly predict the depth of a bronchoscope's navigable route in advance represents a substantial benefit of optimal route planning.

Airway tree segmentation in serial block-face cryomicrotome images of rat lungs

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 μm, 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 μm and 1.005, respectively. The developed approach enables the quantitative studies of physiology and lung diseases in rats, requiring detailed geometric airway models.

A three-stage method for the 3D reconstruction of the tracheobronchial tree from CT scans

This paper proposes a method for segmenting the airways from CT scans of the chest to obtain a 3D model that can be used in the virtual bronchoscopy for the exploration and the planning of paths to the lesions. The method is composed of 3 stages: a gross segmentation that reconstructs the main airway tree using adaptive region growing, a finer segmentation that identifies any potential airway region based on a 2D process that enhances bronchi walls using local information, and a final process to connect any isolated bronchus to the main airways using a morphologic reconstruction process and a path planning technique. The paper includes two examples for the evaluation and discussion of the proposal.

Interactive CT-video registration for the continuous guidance of bronchoscopy

Bronchoscopy is a major step in lung cancer staging. To perform bronchoscopy, the physician uses a procedure plan, derived from a patient's 3D computed-tomography (CT) chest scan, to navigate the bronchoscope through the lung airways. Unfortunately, physicians vary greatly in their ability to perform bronchoscopy. As a result, image-guided bronchoscopy systems, drawing upon the concept of CT-based virtual bronchoscopy (VB), have been proposed. These systems attempt to register the bronchoscope's live position within the chest to a CT-based virtual chest space. Recent methods, which register the bronchoscopic video to CT-based endoluminal airway renderings, show promise but do not enable continuous real-time guidance. We present a CT-video registration method inspired by computer-vision innovations in the fields of image alignment and image-based rendering. In particular, motivated by the Lucas-Kanade algorithm, we propose an inverse-compositional framework built around a gradient-based optimization procedure. We next propose an implementation of the framework suitable for image-guided bronchoscopy. Laboratory tests, involving both single frames and continuous video sequences, demonstrate the robustness and accuracy of the method. Benchmark timing tests indicate that the method can run continuously at 300 frames/s, well beyond the real-time bronchoscopic video rate of 30 frames/s. This compares extremely favorably to the ≥ 1 s/frame speeds of other methods and indicates the method's potential for real-time continuous registration. A human phantom study confirms the method's efficacy for real-time guidance in a controlled setting, and, hence, points the way toward the first interactive CT-video registration approach for image-guided bronchoscopy. Along this line, we demonstrate the method's efficacy in a complete guidance system by presenting a clinical study involving lung cancer patients.

3D TRACHEOBRONCHIAL AIRWAY TREE SEGMENTATION FROM THORAX CT IMAGES

Segmentation of the lung structures is an important operation in the medical analysis. This paper is proposing a region growing algorithm for airway segmentation. The proposed method for the airway tree segmentation works fully in 3D and performs the measurements in the original gray-scale volume for increased accuracy and efficiency. This algorithm uses region growing and morphological operators. The airway segmentation algorithm is intended to serve qualitative and quantitative purposes, and additional three descriptors are being used for evaluation of the airway segmentation. The proposed method was evaluated using the database of 15 patients who underwent lung CT scans, with varying image quality and anatomical changes. Overlap measure is used to show the difference between measured volumes from the established gold standard and the proposed method. The student t-test and Pearson test showed high correlation of the results with the gold standard. Overall, the test results were satisfactory since accurate segmentation was achieved in 95% of the patients.

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

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

Three-dimensional airway tree architecture and pulmonary function

RATIONALE AND OBJECTIVES

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

Extraction of airways from CT (EXACT'09)

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

Computer-based route-definition system for peripheral bronchoscopy

Multi-detector computed tomography (MDCT) scanners produce high-resolution images of the chest. Given a patient's MDCT scan, a physician can use an image-guided intervention system to first plan and later perform bronchoscopy to diagnostic sites situated deep in the lung periphery. An accurate definition of complete routes through the airway tree leading to the diagnostic sites, however, is vital for avoiding navigation errors during image-guided bronchoscopy. We present a system for the robust definition of complete airway routes suitable for image-guided bronchoscopy. The system incorporates both automatic and semiautomatic MDCT analysis methods for this purpose. Using an intuitive graphical user interface, the user invokes automatic analysis on a patient's MDCT scan to produce a series of preliminary routes. Next, the user visually inspects each route and quickly corrects the observed route defects using the built-in semiautomatic methods. Application of the system to a human study for the planning and guidance of peripheral bronchoscopy demonstrates the efficacy of the system.

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

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

Segmentation of the central-chest lymph nodes in 3D MDCT images

Central-chest lymph nodes play a vital role in lung-cancer staging. The definition of lymph nodes from three-dimensional (3D) multidetector computed-tomography (MDCT) images, however, remains an open problem. We propose two methods for computer-based segmentation of the central-chest lymph nodes from a 3D MDCT scan: the single-section live wire and the single-click live wire. For the single-section live wire, the user first applies the standard live wire to a single two-dimensional (2D) section after which automated analysis completes the segmentation process. The single-click live wire is similar but is almost completely automatic. Ground-truth studies involving human 3D MDCT scans demonstrate the robustness, efficiency, and intra-observer and inter-observer reproducibility of the methods.