Wei Qian

Agile convolutional neural network for pulmonary nodule classification using CT images

ObjectiveTo distinguish benign from malignant pulmonary nodules using CT images is critical for their precise diagnosis and treatment. A new Agile convolutional neural network (CNN) framework is proposed to conquer the challenges of a small-scale medical image database and the small size of the nodules, and it improves the performance of pulmonary nodule classification using CT images.MethodsA hybrid CNN of LeNet and AlexNet is constructed through combining the layer settings of LeNet and the parameter settings of AlexNet. A dataset with 743 CT image nodule samples is built up based on the 1018 CT scans of LIDC to train and evaluate the Agile CNN model. Through adjusting the parameters of the kernel size, learning rate, and other factors, the effect of these parameters on the performance of the CNN model is investigated, and an optimized setting of the CNN is obtained finally.ResultsAfter finely optimizing the settings of the CNN, the estimation accuracy and the area under the curve can reach 0.822 and 0.877, respectively. The accuracy of the CNN is significantly dependent on the kernel size, learning rate, training batch size, dropout, and weight initializations. The best performance is achieved when the kernel size is set to

SIMULATION ANALYSIS OF DEFORMATION AND STRESS OF TRACHEAL AND MAIN BROCHIAL WALL FOR SUBJECTS WITH LEFT PULMONARY ARTERY SLING

7\times 7

Lung nodule classification using local kernel regression models with out-of-sample extension

7×7, the learning rate is 0.005, the batch size is 32, and dropout and Gaussian initialization are used.ConclusionsThis competitive performance demonstrates that our proposed CNN framework and the optimization strategy of the CNN parameters are suitable for pulmonary nodule classification characterized by small medical datasets and small targets. The classification model might help diagnose and treat pulmonary nodules effectively.

Particle Disposition in the Realistic Airway Tree Models of Subjects with Tracheal Bronchus and COPD

Using computational fluid dynamics (CFD) method, the feasibility of simulating transient airflow in a CT-based airway tree with more than 100 outlets for a whole respiratory period is studied, and the influence of truncations of terminal bronchi on CFD characteristics is investigated. After an airway model with 122 outlets is extracted from CT images, the transient airflow is simulated. Spatial and temporal variations of flow velocity, wall pressure, and wall shear stress are presented; the flow pattern and lobar distribution of air are gotten as well. All results are compared with those of a truncated model with 22 outlets. It is found that the flow pattern shows lobar heterogeneity that the near-wall air in the trachea is inhaled into the upper lobe while the center flow enters the other lobes, and the lobar distribution of air is significantly correlated with the outlet area ratio. The truncation decreases airflow to right and left upper lobes and increases the deviation of airflow distributions between inspiration and expiration. Simulating the transient airflow in an airway tree model with 122 bronchi using CFD is feasible. The model with more terminal bronchi decreases the difference between the lobar distributions at inspiration and at expiration.

Semi-supervised Nonnegative Matrix Factorization with Commonness Extraction

Left pulmonary artery sling (LPAS) is a kind of severe congenital anomaly, where the stenoses usually occur at trachea and main bronchi for the external compression of the artery sling. Computed tomography (CT) images can provide accurate morphological analysis, but the airflow and its effects on the airway wall are unknown and seldom investigated. In the present study, a uni-directional coupling fluid–structure interaction (UCFSI) method is employed to simulate the deformation and stress of tracheal and main bronchial wall for four LPAS subjects and one health control. Much higher airflow velocity is observed for LPAS subjects due to the stenosis, and the deformation and equivalent stress of airway wall are about 50–900 and 90–1000 times of the health control, respectively. The direction of tracheal shift may be related to the airway shape, and is opposite to the net reaction force. The influences of inlet flow velocity and wall thickness on the deformation and stress are significant and their relationsh...

Automatic segmentation of juxta-pleural tumors from CT images based on morphological feature analysis.

Tracheal Bronchus (TB) is a rare congenital anomaly characterized by the presence of an abnormal bronchus originating from the trachea or main bronchi and directed toward the upper lobe. The airflow pattern in tracheobronchial trees of TB subjects is critical, but has not been systemically studied. This study proposes to simulate the airflow using CT image based models and the computational fluid dynamics (CFD) method. Six TB subjects and three health controls (HC) are included. After the geometric model of tracheobronchial tree is extracted from CT images, the spatial distribution of velocity, wall pressure, wall shear stress (WSS) is obtained through CFD simulation, and the lobar distribution of air, flow pattern and global pressure drop are investigated. Compared with HC subjects, the main bronchus angle of TB subjects and the variation of volume are large, while the cross-sectional growth rate is small. High airflow velocity, wall pressure, and WSS are observed locally at the tracheal bronchus, but the global patterns of these measures are still similar to those of HC. The ratio of airflow into the tracheal bronchus accounts for 6.6–15.6% of the inhaled airflow, decreasing the ratio to the right upper lobe from 15.7–21.4% (HC) to 4.9–13.6%. The air into tracheal bronchus originates from the right dorsal near-wall region of the trachea. Tracheal bronchus does not change the global pressure drop which is dependent on multiple variables. Though the tracheobronchial trees of TB subjects present individualized features, several commonalities on the structural and airflow characteristics can be revealed. The observed local alternations might provide new insight into the reason of recurrent local infections, cough and acute respiratory distress related to TB.

Using Numerical Simulations and Experiments to Compare Different Pure Mathematical Models for Analyzing Dynamic Contrast Enhanced MRI Data

Abstract Computer-aided classification is a major research task for computer-aided diagnosis of pulmonary nodules. In radiology domain, labeled data can be expensive to generate. Therefore, in this study, a novel unsupervised spectral clustering algorithm was presented to distinguish benign and malignant nodules. In this algorithm, a new Laplacian matrix was constructed by using local kernel regression models (LKRM) and incorporating a regularization term, the regularization term can tackle the out-of-sample problem. To verify the feasibility of our algorithm, a ground truth dataset was assembled from the LIDC-IDRI database, including 371 benign and 375 malignant lung nodules. All nodules were represented by the texture features, which were computed from the regions of interest (ROIs). Extensive experiments on lung nodules showed that the proposed algorithm not only achieved a higher classification performance than existing popular unsupervised algorithms, but also had superiority comparing to some supervised algorithms (linear discriminant analysis and extreme learning machine).

A Content-based Image Retrieval Scheme for Lung Nodule Classification

Dispositions of inhalable particles in the human respiratory tract trigger and exacerbate airway inflammatory diseases. However, the particle deposition (PD) in airway of subjects with tracheal bronchus (TB) and chronic obstructive pulmonary diseases (COPD) is unknown. We therefore propose to clarify the disrupted PD associated with TB and COPD using the computational fluid dynamics (CFD) simulation. Totally nine airway tree models are included. Six are extracted from CT images of different individuals (two with TB, two with COPD, and two healthy controls (HC)). The others are the artificially modified models (AMMs) generated by the virtual lesion. Specifically, they are constructed through artificially adding a tracheal bronchus or a stenosis on one HC model. The deposition efficiency (DE) and deposition fraction (DF) in these models are obtained by the Euler-Lagrange approach, analyzed, and compared across models, locations, and particle sizes (0.1-10.0 micrometers). It is found that the PD in models with TB and COPD has been disrupted by the geometrical changes and followed airflow alternations. DE of the tracheal bronchus is higher for TB models. For COPD, the stenosis location determines the effects on DE and DF. Higher DF at the trachea is observed in TB1, TB2, and COPD2 models. DE increases with the particle size, and DE of the terminal bronchi is higher than that of central regions. Combined with AMMs, the CFD simulation using realistic airway models demonstrates disruptions of DP. The methods and findings might help understand the etiology of pulmonary diseases and improve the efficacy of inhaled medicines.