Pulmonary Nodule Detection System Research Articles

Diagnostic Value of Artificial Intelligence Based on CT Image in Benign and Malignant Pulmonary Nodules.

Objective To evaluate the diagnostic value of artificial intelligence-assisted CT imaging in benign and malignant pulmonary nodules. Methods The CT scan screening of pulmonary nodules from November 2018 to November 2020 was retrospectively collected. The diagnosis of pulmonary nodules and surgical treatment were performed. A total of 194 nodules in 152 patients with clear pathological results were observed. All patients underwent CT examination to analyze the consistency of the results of artificial intelligence, physician reading according to imaging features, multidisciplinary team work (MDT) diagnosis, and postoperative pathological results; the diagnostic efficacy of different diagnostic methods for solitary pulmonary nodules and the differences of ROC curve and AUC were analyzed. The accuracy, specificity, sensitivity, positive predictive value, negative predictive value, false negative rate, and false positive rate of different diagnostic methods for pulmonary nodules were calculated, and the ROC curves of different diagnostic methods were plotted. Results The accuracy, sensitivity, specificity, and Youden index of artificial intelligence (AI) were 89.69%, 92.98%, 65.22%, and 58.20%; the accuracy, sensitivity, specificity, and Youden index of physician reading were 85.57%, 88.30%, 65.22%, and 53.52%; the accuracy, sensitivity, specificity, and Youden index of MDT were 96.91%, 98.25%, 86.96%, and 85.21%, respectively. The kappa values of artificial intelligence, physician reading, and MDT were 0.541, 0.437, and 0.852, and the AUC was 0.768, 0.791, and 0.926, respectively (P < 0.001). The average detection time of pulmonary nodules in the AI group, manual reading group, and MAT group was (145 ± 97) s, (534 ± 297) s, and (421 ± 128) s (P < 0.001). Conclusion Artificial intelligence pulmonary nodule detection system can improve the coincidence rate and accuracy of early diagnosis of lung cancer, shorten the average detection time, and provide more accurate information for clinical decision-making.

Adoption of computerized tomography images in detection of lung nodules and analysis of neuropeptide correlative substances under deep learning algorithm

To solve the problems of low sensitivity and high false positive rate of current lung nodule detection technology, and to explore the correlation between neuropeptide correlative substances and lung nodules, in the study, the deep learning-based computerized tomography (CT) images were used to detect lung nodules. Specifically, a multi-scale three-dimensional convolutional neural network (CNN) was established for lung nodule detection. First, high-quality candidate nodules were obtained by a two-dimensional CNN. Then, three-dimensional CNNs of different scales were constructed for candidate nodules of different sizes, and the fused model was used to classify candidate nodules. Finally, the detection results of the single network, the two-dimensional CNN, and the fused network were compared. Meanwhile, the serum levels of vasoactive intestinal peptide (VIP) and substance P (SP) were detected in patients with lung injury caused by lung nodules, so as to analyze the correlation between neuropeptide correlative substances and lung nodules. The results showed that the detection sensitivity of the fused network was 85.8% and 92.9% when the number of false positives was 1 and 4, respectively, which were higher than that of the single network and two-dimensional CNN. However, the content of VIP in the serum of patients with pulmonary nodules was significantly reduced, while the content of SP was significantly increased. Therefore, the algorithm proposed in this study can effectively improve the sensitivity of the pulmonary nodule detection system and reduce false positive rate; at the same time, neuropeptide correlative substances may be correlated with the lung injury caused by pulmonary nodules.

Pulmonary nodule detection using hybrid two-stage 3D CNNs.

Early detection of pulmonary nodules is an effective way to improve patients' chances of survival. In this work, we propose a novel and efficient way to build a computer-aided detection (CAD) system for pulmonary nodules based on computed tomography (CT) scans. The system can be roughly divided into two steps: nodule candidate detection and false positive reduction. Considering the three-dimensional (3D) nature of nodules, the CAD system adopts 3D convolutional neural networks (CNNs) in both stages. Specifically, in the first stage, a segmentation-based 3D CNN with a hybrid loss is designed to segment nodules. According to the probability maps produced by the segmentation network, a threshold method and connected component analysis are applied to generate nodule candidates. In the second stage, we employ three classification-based 3D CNNs with different types of inputs to reduce false positives. In addition to simple raw data input, we also introduce hybrid inputs to make better use of the output of the previous segmentation network. In experiments, we use data augmentation and batch normalization to avoid overfitting. We evaluate the system on 888 CT scans from the publicly available LIDC-IDRI dataset, and our method achieves the best performance by comparing with the state-of-the-art methods, which has a high detection sensitivity of 97.5% with an average of only one false positive per scan. An additional evaluation on 115 CT scans from local hospitals is also performed. Experimental results demonstrate that our method is highly suited for the detection of pulmonary nodules.

Light Deep Model for Pulmonary Nodule Detection from CT Scan Images for Mobile Devices

The emergence of cognitive computing and big data analytics revolutionize the healthcare domain, more specifically in detecting cancer. Lung cancer is one of the major reasons for death worldwide. The pulmonary nodules in the lung can be cancerous after development. Early detection of the pulmonary nodules can lead to early treatment and a significant reduction of death. In this paper, we proposed an end-to-end convolutional neural network- (CNN-) based automatic pulmonary nodule detection and classification system. The proposed CNN architecture has only four convolutional layers and is, therefore, light in nature. Each convolutional layer consists of two consecutive convolutional blocks, a connector convolutional block, nonlinear activation functions after each block, and a pooling block. The experiments are carried out using the Lung Image Database Consortium (LIDC) database. From the LIDC database, 1279 sample images are selected of which 569 are noncancerous, 278 are benign, and the rest are malignant. The proposed system achieved 97.9% accuracy. Compared to other famous CNN architecture, the proposed architecture has much lesser flops and parameters and is thereby suitable for real-time medical image analysis.

Pulmonary Nodule Detection Based on ISODATA-Improved Faster RCNN and 3D-CNN with Focal Loss

The early diagnosis of pulmonary cancer can significantly improve the survival rate of patients, where pulmonary nodules detection in computed tomography images plays an important role. In this article, we propose a novel pulmonary nodule detection system based on convolutional neural networks (CNN). Our system consists of two stages, pulmonary nodule candidate detection and false positive reduction. For candidate detection, we introduce Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) to Faster Region-based Convolutional Neural Network (Faster R-CNN) model. For false positive reduction, a three-dimensional convolutional neural network (3D-CNN) is employed to completely utilize the three-dimensional nature of CT images. In this network, Focal Loss is used to solve the class imbalance problem in this task. Experiments were conducted on LUNA16 dataset. The results show the preferable performance of the proposed system and the effectiveness of using ISODATA and Focal loss in pulmonary nodule detection is proved.

Automated pulmonary nodule detection system in computed tomography images based on Active-contour and SVM classification algorithm

Lung cancer is a common type of cancer that requires early diagnosis. Computer systems by particular different image processing techniques can use for increase the speed and accuracy of lung nodule detection. CT images used in this work in order to process medical images. In this paper proposed an automatic lung nodule detection algorithm using active contour method and SVM classification method. In proposed method, at first in order to achieve better results, lung CT image pre-processing is performed. Then the lung area is segmented by thresholding method followed by some reconstruction techniques to transfer non-isolated nodules into isolated ones. In the next step the nodule candidates are determined using active contour method. Then, nodules are detected by the support vector machine (SVM) classifier using efficient 2D stochastic and 3D anatomical features. In the result, nodules are detected with an overall detection rate of 87%; the number of false positive is 7.5/scan and the location of all detected nodules are recognized correctly.

3D deep learning for detecting pulmonary nodules in CT scans.

To demonstrate and test the validity of a novel deep-learning-based system for the automated detection of pulmonary nodules. The proposed system uses 2 3D deep learning models, 1 for each of the essential tasks of computer-aided nodule detection: candidate generation and false positive reduction. A total of 888 scans from the LIDC-IDRI dataset were used for training and evaluation. Results for candidate generation on the test data indicated a detection rate of 94.77% with 30.39 false positives per scan, while the test results for false positive reduction exhibited a sensitivity of 94.21% with 1.789 false positives per scan. The overall system detection rate on the test data was 89.29% with 1.789 false positives per scan. An extensive and rigorous validation was conducted to assess the performance of the proposed system. The system demonstrated a novel combination of 3D deep neural network architectures and demonstrates the use of deep learning for both candidate generation and false positive reduction to be evaluated with a substantial test dataset. The results strongly support the ability of deep learning pulmonary nodule detection systems to generalize to unseen data. The source code and trained model weights have been made available. A novel deep-neural-network-based pulmonary nodule detection system is demonstrated and validated. The results provide comparison of the proposed deep-learning-based system over other similar systems based on performance.

3D pulmonary nodules detection using fast marching segmentation

Pulmonary nodule detection is an important step in lung cancer detection because nodules arethe alert signal of lung cancer. The early detection of them can hence increase the patient’ssurvival rates. This paper proposes an automated computer aided diagnosis system fordetection of pulmonary nodules based on three dimensional (3D) structures. Lungparenchyma segmentation using fast marching method was employed. A simple thresholdingtechnique is used to extract candidate nodules from the segmented lung parenchyma. A 3Dimage of nodule candidates is reconstructed by mean of stacked 2D images. To find theconnected voxels of a blob, a 3D connected component labelling is used. Features extractedfrom each blob are then fed into the classifier. The random forest algorithm has been invokedfor nodule and non-nodule classification. The proposed detection methodology can give theaccuracy of 92%.Keywords: lung cancer; pulmonary nodule; fast marching; 3D features; random forestclassifier.

3D skeletonization feature based computer-aided detection system for pulmonary nodules in CT datasets

Pulmonary nodule detection has a significant impact on early diagnosis of lung cancer. To effectively detect pulmonary nodules from interferential vessels in chest CT datasets, this paper proposes a novel 3D skeletonization feature, named as voxels remove rate. Based on this feature, a computer-aided detection system is constructed to validate its performance. The system mainly consists of five stages. Firstly, the lung tissues are segmented by a global optimal active contour model, which can extract all structures (including juxta-pleural nodules) in the lung region. Secondly, thresholding, 3D binary morphological operations, and 3D connected components labeling are utilized to extract candidates of pulmonary nodules. Thirdly, combining the voxels remove rate with other nine existing 3D features (including gray features and shape features), the extracted candidates are characterized. Then, prior anatomical knowledge is utilized for preliminary screening of numerous invalid nodule candidates. Finally, false positives are reduced by support vector machine. Our system is evaluated on early stage lung cancer subjects obtained from the publicly available LIDC-IDRI database. The result shows the proposed 3D skeletonization feature is a useful indicator that efficiently differentiates lung nodules from the other suspicious structures. The computer-aided detection system based on this feature can detect various types of nodules, including solitary, juxta-pleural and juxta-vascular nodules.

Automated Pulmonary Lung Nodule Detection Using an Optimal Manifold Statistical Based Feature Descriptor and SVM Classifier

The pulmonary lung nodule is the most common indicator of lung cancer. An efficient automated pulmonary nodule detection system aids the radiologists to detect the lung abnormalities at an early stage. In this paper, an automated lung nodule detection system using a feature descriptor based on optimal manifold statistical thresholding to segment lung nodules in Computed Tomography (CT) scans is presented. The system comprises three processing stages. In the first stage, the lung region is extracted from thoracic CT scans using gray level thresholding and 3D connected component labeling. After that novel lung contour correction method is proposed using modified convex hull algorithm to correct the border of a diseased lung. In the second stage, optimal manifold statistical image thresholding is described to minimize the discrepancy between nodules and other tissues of the segmented lung region. Finally, a set of 2D and 3D features are extracted from the nodule candidates, and then the system is trained by employing support vector machines (SVM) to classify the nodules and non-nodules. The performance of the proposed system is assessed using Lung TIME database. The system is tested on 148 cases containing 36408 slices with total sensitivity of 94.3%, is achieved with only 2.6 false positives per scan.

Computer-aided detection of pulmonary nodules using dynamic self-adaptive template matching and a FLDA classifier

Improving the performance of computer-aided detection (CAD) system for pulmonary nodules is still an important issue for its future clinical applications. This study aims to develop a new CAD scheme for pulmonary nodule detection based on dynamic self-adaptive template matching and Fisher linear discriminant analysis (FLDA) classifier. We first segment and repair lung volume by using OTSU algorithm and three-dimensional (3D) region growing. Next, the suspicious regions of interest (ROIs) are extracted and filtered by applying 3D dot filtering and thresholding method. Then, pulmonary nodule candidates are roughly detected with 3D dynamic self-adaptive template matching. Finally, we optimally select 11 image features and apply FLDA classifier to reduce false positive detections. The performance of the new method is validated by comparing with other methods through experiments using two groups of public datasets from Lung Image Database Consortium (LIDC) and ANODE09. By a 10-fold cross-validation experiment, the new CAD scheme finally has achieved a sensitivity of 90.24% and a false-positive (FP) of 4.54 FP/scan on average for the former dataset, and a sensitivity of 84.1% with 5.59 FP/scan for the latter. By comparing with other previously reported CAD schemes tested on the same datasets, the study proves that this new scheme can yield higher and more robust results in detecting pulmonary nodules.

Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks.

We propose a novel Computer-Aided Detection (CAD) system for pulmonary nodules using multi-view convolutional networks (ConvNets), for which discriminative features are automatically learnt from the training data. The network is fed with nodule candidates obtained by combining three candidate detectors specifically designed for solid, subsolid, and large nodules. For each candidate, a set of 2-D patches from differently oriented planes is extracted. The proposed architecture comprises multiple streams of 2-D ConvNets, for which the outputs are combined using a dedicated fusion method to get the final classification. Data augmentation and dropout are applied to avoid overfitting. On 888 scans of the publicly available LIDC-IDRI dataset, our method reaches high detection sensitivities of 85.4% and 90.1% at 1 and 4 false positives per scan, respectively. An additional evaluation on independent datasets from the ANODE09 challenge and DLCST is performed. We showed that the proposed multi-view ConvNets is highly suited to be used for false positive reduction of a CAD system.

Artificial Neural Network based Classification of Lungs Nodule using Hybrid Features from Computerized Tomographic Images

An automated pulmonary nodule detection system is necessary to help radiologist to identify and detect the nodules at early stage. In this paper, a novel pulmonary nodule detection system is proposed using Artificial Neural Networks (ANN) based on hybrid features consist of 2D and 3D Geometric and Intensity based statistical features. The lung volume is segmented using thresholding, 3D connected component labeling, contour correction and morphological operators. The candidate nodules are extracted and pruned based on the rules that are built using characteristics of nodules. The 2D and 3D Geometric features and Intensity Based Statistical features are extracted and used to train a Neural Network. The proposed Computer-Aided Diagnostic (CAD) system is tested and validated using standard dataset of Lung Image Consortium Database (LIDC). The results obtained from proposed CAD system are good as compared to existing CAD systems. The sensitivity of 96.95% is achieved with accuracy of 96.68%.

A Computer Aided Pulmonary Nodule Detection System Using Multiple Massive Training SVMs

A computer aided pulmonary nodule detection system for chest radiography is proposed. The system consists of three models, viz., lung segmentation, lung nodule candidates detection and false positive reduction. Several innovations are offered in this system. The first one is that the detection of potential lung nodule candidates is conceived as a filtering process that searches for any region which has a spherical structure (where a potential nodule may happen to occur) in a chest radiograph by analysis eignvalues of Hessian matrix of an image. The second one is that a novel two stage classifier based method is developed to address the problem of false positive reduction in lung nodule detection, in which a rules based classifier is followed by a filter termed as multiple massive training supported vector machine (MTSVM), where the rules based classifier is employed to quickly remove obvious FP (outliers) so that their influence on the training of MTSVM was eliminated, the MTSVM is developed to further separate nodules from nonnodule candidates. Experimental results suggest that the proposed CAD scheme was superior to others in FPs reduction of lung nodule detection in chest radiography.

Automated Pulmonary Nodule Detection System in Computed Tomography Images: A Hierarchical Block Classification Approach

A computer-aided detection (CAD) system is helpful for radiologists to detect pulmonary nodules at an early stage. In this paper, we propose a novel pulmonary nodule detection method based on hierarchical block classification. The proposed CAD system consists of three steps. In the first step, input computed tomography images are split into three-dimensional block images, and we apply entropy analysis on the block images to select informative blocks. In the second step, the selected block images are segmented and adjusted for detecting nodule candidates. In the last step, we classify the nodule candidate images into nodules and non-nodules. We extract feature vectors of the objects in the selected blocks. Lastly, the support vector machine is applied to classify the extracted feature vectors. Performance of the proposed system is evaluated on the Lung Image Database Consortium database. The proposed method has reduced the false positives in the nodule candidates significantly. It achieved 95.28% sensitivity with only 2.27 false positives per scan.

Genetic programming-based feature transform and classification for the automatic detection of pulmonary nodules on computed tomography images

An effective automated pulmonary nodule detection system can assist radiologists in detecting lung abnormalities at an early stage. In this paper, we propose a novel pulmonary nodule detection system based on a genetic programming (GP)-based classifier. The proposed system consists of three steps. In the first step, the lung volume is segmented using thresholding and 3D-connected component labeling. In the second step, optimal multiple thresholding and rule-based pruning are applied to detect and segment nodule candidates. In this step, a set of features is extracted from the detected nodule candidates, and essential 3D and 2D features are subsequently selected. In the final step, a GP-based classifier (GPC) is trained and used to classify nodules and non-nodules. GP is suitable for detecting nodules because it is a flexible and powerful technique; as such, the GPC can optimally combine the selected features, mathematical functions, and random constants. Performance of the proposed system is then evaluated using the Lung Image Database Consortium (LIDC) database. As a result, it was found that the proposed method could significantly reduce the number of false positives in the nodule candidates, ultimately achieving a 94.1% sensitivity at 5.45 false positives per scan.

A Comparison of Four Versions of a Computer-aided Detection System for Pulmonary Nodules on Chest Radiographs

There are few computer-aided detection (CAD) systems that are available for the detection of nodules on chest radiographs. We evaluated the performance of a Food and Drug Administration-approved system and 3 of its subsequent versions to determine their potential to improve readers' accuracy. We tested the performance of 4 generations of CAD software programs, RapidScreen 1.1 and OnGuard 3.0, 4.0, and 5.0 (Riverain Medical), for their ability to detect lung cancer on a sample of 100 patients with and 100 patients without nodules. Each proven nodule (computed tomography scan and/or pathology) was evaluated for its overall difficulty, size, density, shape, contour, and location. The sensitivity and number of false-positive (FP) marks were compared between the different versions; reasons for FP and false-negative marks were analyzed and compared. The newer versions have significantly improved overall sensitivity [62.5% (OnGuard 3.0), 62.5% (4.0), and 64.4% (5.0)] compared with the first version (44.2%). OnGuard 5.0 demonstrated sensitivity of 73.3% for moderately subtle lesions compared with very subtle lesions (20.0%). There was a significant reduction in the average number of FPs per image for each version (3.9 for 1.1, 3.3 for 3.0, 2.6 for 4.0, and 2.0 for 5.0). Rib and vessel crossings were the most common reasons for FPs. The latest version of CAD demonstrates good detection of moderately subtle lesions with a relatively low FP rate.

Computer-Aided Detection in Screening CT for Pulmonary Nodules

Our objective was to evaluate the performance of a computer-aided detection (CAD) system for pulmonary nodule detection using low-dose screening CT images. One hundred fifty consecutive low-dose screening CT examinations were independently evaluated by a radiologist and a CAD pulmonary nodule detection system (R2 Technology) designed to identify nodules larger than 4 mm in maximum long-axis diameter. All discrepancies between the two techniques were reviewed by one of another two radiologists working in consensus with the initial interpreting radiologist, and a "true" nodule count was determined. Detected nodules were classified by size, density, and location. The performance of the initial radiologist and the CAD system were compared. The radiologist detected 518 nodules and the CAD system, 934 nodules. Of the 1,106 separate nodules detected using the two techniques, 628 were classified as true nodules on consensus review. Of the true nodules present, the radiologist detected 518 (82%) of 628 nodules and the CAD, 456 (73%) of 628 nodules. All 518 radiologist-detected nodules were true nodules, and 456 (49%) of 934 of CAD-detected nodules were true nodules. The radiologist missed 110 true nodules that were only detected by CAD. In six patients, these were the only nodules detected in the examination, changing the imaging follow-up protocol. CAD identified 478 lesions that on consensus review were false-positive nodules, a rate of 3.19 (478/150) per patient. CAD detected 72.6% of true nodules and detected nodules in six (4%) patients not identified by radiologists, changing the imaging follow-up protocol of these subjects. In this study, the combined review of low-dose CT scans by both the radiologist and CAD was necessary to identify all nodules.

Clinical evaluation of a pulmonary nodule detection system

Purpose To evaluate the effect of the prototype version of a computer-aided detection system, the so-called Lung-CAD, on the sensitivity of a cohort of community radiologists concerning the detection of any pulmonary nodules on multi-detector raw computer tomography (MDCT) datasets. Materials and methods 16 MDCT studies with section thickness ranging from 1.00 to 1.25 mm were evaluated by 6 community level radiologists of two different sites. The readers were instructed to mark every nodule of size > 3 mm. Consecutively, the datasets were processed with the Lung-CAD-prototype version. Then the community radiologists reviewed and rejected/accepted the computer detected nodules. The Ground truth was defined as any nodule found by the reader and/or the CAD-prototype and also validated by one single expert's blinded review. Conclusion The CAD-assisted work significantly increased the readers' sensitivity without any significant change on the rate of false positive findings per case. Also of equal importance, the interreader variability of sensitivity was reduced, leading to a more predictable detection rate across radiologists.

Detection of simulated pulmonary nodules by single-exposure dual-energy computed radiography of the chest: effect of a computer-aided diagnosis system (Part 2)

Objective: To evaluate the performance of the computer-aided diagnosis (CAD) scheme on the detection of pulmonary nodules (PNs) in single-exposure dual-energy subtraction computed radiography (CR) images of the chest, and to evaluate the effect of this CAD scheme on radiologists' detectabilities. Methods and material: We compared the detectability by the CAD scheme with the detectability by 12 observers by using conventional CR (C-CR) and bone-subtracted CR (BS-CR) images of 25 chest phantoms with a low-contrast nylon nodule. Results: Both in the CAD scheme and for the observers, the detectability of BS-CR images was superior to that of C-CR images ( P<0.005). The detection performance of the CAD scheme was equal to that of the observers. The nodules detected by the CAD did not necessarily coincide with those by the observers. Thus, if observers can use the results of the CAD system as a ‘second opinion’, their detectabilities increase. Conclusion: The CAD system for detection of PNs in the single-exposure dual-energy subtraction method is promising for improving radiologists' detectabilities of PNs.