LGDNet: local feature coupling global representations network for pulmonary nodules detection

Detection of suspicious pulmonary nodules from lung CT scans is a crucial task in computer-aided diagnosis (CAD) systems. In recent years, various deep learning-based approaches have been proposed and demonstrated significant potential for addressing this task. However, existing deep convolutional neural networks exhibit limited long-range dependency capabilities and neglect crucial contextual information, resulting in reduced performance on detecting small-size nodules in CT scans. In this work, we propose a novel end-to-end framework called LGDNet for the detection of suspicious pulmonary nodules in lung CT scans by fusing local features and global representations. To overcome the limited long-range dependency capabilities inherent in convolutional operations, a dual-branch module is designed to integrate the convolutional neural network (CNN) branch that extracts local features with the transformer branch that captures global representations. To further address the issue of misalignment between local features and global representations, an attention gate module is proposed in the up-sampling stage to selectively combine misaligned semantic data from both branches, resulting in more accurate detection of small-size nodules. Our experiments on the large-scale LIDC dataset demonstrate that the proposed LGDNet with the dual-branch module and attention gate module could significantly improve the nodule detection sensitivity by achieving a final competition performance metric (CPM) score of 89.49%, outperforming the state-of-the-art nodule detection methods, indicating its potential for clinical applications in the early diagnosis of lung diseases.

TiCNet: Transformer in Convolutional Neural Network for Pulmonary Nodule Detection on CT Images

Lung cancer is the leading cause of cancer death. Since lung cancer appears as nodules in the early stage, detecting the pulmonary nodules in an early phase could enhance the treatment efficiency and improve the survival rate of patients. The development of computer-aided analysis technology has made it possible to automatically detect lung nodules in Computed Tomography (CT) screening. In this paper, we propose a novel detection network, TiCNet. It is attempted to embed a transformer module in the 3D Convolutional Neural Network (CNN) for pulmonary nodule detection on CT images. First, we integrate the transformer and CNN in an end-to-end structure to capture both the short- and long-range dependency to provide rich information on the characteristics of nodules. Second, we design the attention block and multi-scale skip pathways for improving the detection of small nodules. Last, we develop a two-head detector to guarantee high sensitivity and specificity. Experimental results on the LUNA16 dataset and PN9 dataset showed that our proposed TiCNet achieved superior performance compared with existing lung nodule detection methods. Moreover, the effectiveness of each module has been proven. The proposed TiCNet model is an effective tool for pulmonary nodule detection. Validation revealed that this model exhibited excellent performance, suggesting its potential usefulness to support lung cancer screening.

DBPNDNet: dual-branch networks using 3DCNN toward pulmonary nodule detection

With the advancement of artificial intelligence, CNNs have been successfully introduced into the discipline of medical data analyzing. Clinically, automatic pulmonary nodules detection remains an intractable issue since those nodules existing in the lung parenchyma or on the chest wall are tough to be visually distinguished from shadows, background noises, blood vessels, and bones. Thus, when making medical diagnosis, clinical doctors need to first pay attention to the intensity cue and contour characteristic of pulmonary nodules, so as to locate the specific spatial locations of nodules. To automate the detection process, we propose an efficient architecture of multi-task and dual-branch 3D convolution neural networks, called DBPNDNet, for automatic pulmonary nodule detection and segmentation. Among the dual-branch structure, one branch is designed for candidate region extraction of pulmonary nodule detection, while the other incorporated branch is exploited for lesion region semantic segmentation of pulmonary nodules. In addition, we develop a 3D attention weighted feature fusion module according to the doctor's diagnosis perspective, so that the captured information obtained by the designed segmentation branch can further promote the effect of the adopted detection branch mutually. The experiment has been implemented and assessed on the commonly used dataset for medical image analysis to evaluate our designed framework. On average, our framework achieved a sensitivity of 91.33% false positives per CT scan and reached 97.14% sensitivity with 8 FPs per scan. The results of the experiments indicate that our framework outperforms other mainstream approaches.

Pulmonary nodule detection based on IR-UNet + +

Lung cancer is one of the cancers with the highest incidence rate and death rate worldwide. An initial lesion of the lung appears as nodules in the lungs on CT images, and early and timely diagnosis can greatly improve the survival rate. Automatic detection of lung nodules can greatly improve work efficiency and accuracy rate. However, owing to the three-dimensional complex structure of lung CT data and the variation in shapes and appearances of lung nodules, high-precision detection of pulmonary nodules remains challenging. To address the problem, a new 3D framework IR-UNet +  + is proposed for automatic pulmonary nodule detection in this paper. First, the Inception Net and ResNet are combined as the building blocks. Second, the squeeze-and-excitation structure is introduced into building blocks for better feature extraction. Finally, two short skip pathways are redesigned based on the U-shaped network. To verify the effectiveness of our algorithm, systematic experiments are conducted on the LUNA16 dataset. Experimental results show that the proposed network performs better than several existing lung nodule detection methods with the sensitivity of 1 FP/scan, 4 FPs/scan, and 8 FPs/scan being 90.13%, 94.77%, and 95.78%, respectively. Therefore, it comes to the conclusion that our proposed model has achieved superior performance for lung nodule detection.

Cross attention guided multi-scale feature fusion for false-positive reduction in pulmonary nodule detection

False-positive reduction is a crucial step of computer-aided diagnosis (CAD) system for pulmonary nodules detection and it plays an important role in lung cancer diagnosis. In this paper, we propose a novel cross attention guided multi-scale feature fusion method for false-positive reduction in pulmonary nodule detection. Specifically, a 3D SENet50 fed with a candidate nodule cube is applied as the backbone to acquire multi-scale coarse features. Then, the coarse features are refined and fused by the multi-scale fusion part to achieve a better feature extraction result. Finally, a 3D spatial pyramid pooling module is used to enhance receptive field and a distributed aligned linear classifier is applied to get the confidence score. In addition, each of the five nodule cubes with different sizes centering on every testing nodule position is fed into the proposed framework to obtain a confidence score separately and a weighted fusion method is used to improve the generalization performance of the model. Extensive experiments are conducted to demonstrate the effectiveness of the classification performance of the proposed model. The data used in our work is from the LUNA16 pulmonary nodule detection challenge. In this data set, the number of true-positive pulmonary nodules is 1,557, while the number of false-positive ones is 753,418. The new method is evaluated on the LUNA16 dataset and achieves the score of the competitive performance metric (CPM) 84.8%.

[A three dimensional convolutional neural network pulmonary nodule detection algorithm based on the multi-scale attention mechanism]

Early screening based on computed tomography (CT) pulmonary nodule detection is an important means to reduce lung cancer mortality, and in recent years three dimensional convolutional neural network (3D CNN) has achieved success and continuous development in the field of lung nodule detection. We proposed a pulmonary nodule detection algorithm by using 3D CNN based on a multi-scale attention mechanism. Aiming at the characteristics of different sizes and shapes of lung nodules, we designed a multi-scale feature extraction module to extract the corresponding features of different scales. Through the attention module, the correlation information between the features was mined from both spatial and channel perspectives to strengthen the features. The extracted features entered into a pyramid-similar fusion mechanism, so that the features would contain both deep semantic information and shallow location information, which is more conducive to target positioning and bounding box regression. On representative LUNA16 datasets, compared with other advanced methods, this method significantly improved the detection sensitivity, which can provide theoretical reference for clinical medicine.

An efficient multi-path 3D convolutional neural network for false-positive reduction of pulmonary nodule detection

PURPOSE

Considering that false-positive and true pulmonary nodules are highly similar in shapes and sizes between lung computed tomography scans, we develop and evaluate a false-positive nodules reduction method applied to the computer-aided diagnosis system.

METHODS

To improve the pulmonary nodule diagnosis quality, a 3D convolutional neural networks (CNN) model is constructed to effectively extract spatial information of candidate nodule features through the hierarchical architecture. Furthermore, three paths corresponding to three receptive field sizes are adopted and concatenated in the network model, so that the feature information is fully extracted and fused to actively adapting to the changes in shapes, sizes, and contextual information between pulmonary nodules. In this way, the false-positive reduction is well implemented in pulmonary nodule detection.

RESULTS

Multi-path 3D CNN is performed on LUNA16 dataset, which achieves an average competitive performance metric score of 0.881, and excellent sensitivity of 0.952 and 0.962 occurs to 4, 8 FP/Scans.

CONCLUSION

By constructing a multi-path 3D CNN to fully extract candidate target features, it accurately identifies pulmonary nodules with different sizes, shapes, and background information. In addition, the proposed general framework is also suitable for similar 3D medical image classification tasks.

Attention-embedded complementary-stream CNN for false positive reduction in pulmonary nodule detection

False positive reduction plays a key role in computer-aided detection systems for pulmonary nodule detection in computed tomography (CT) scans. However, this remains a challenge owing to the heterogeneity and similarity of anisotropic pulmonary nodules. In this study, a novel attention-embedded complementary-stream convolutional neural network (AECS-CNN) is proposed to obtain more representative features of nodules for false positive reduction. The proposed network comprises three function blocks: 1) attention-guided multi-scale feature extraction, 2) complementary-stream block with an attention module for feature integration, and 3) classification block. The inputs of the network are multi-scale 3D CT volumes due to variations in nodule sizes. Subsequently, a gradual multi-scale feature extraction block with an attention module was applied to acquire more contextual information regarding the nodules. A subsequent complementary-stream integration block with an attention module was utilized to learn the significantly complementary features. Finally, the candidates were classified using a fully connected layer block. An exhaustive experiment on the LUNA16 challenge dataset was conducted to verify the effectiveness and performance of the proposed network. The AECS-CNN achieved a sensitivity of 0.92 with 4 false positives per scan. The results indicate that the attention mechanism can improve the network performance in false positive reduction, the proposed AECS-CNN can learn more representative features, and the attention module can guide the network to learn the discriminated feature channels and the crucial information embedded in the data, thereby effectively enhancing the performance of the detection system.

Determination of radiation dose and low-dose protocol for digital chest tomosynthesis using radiophotoluminescent (RPL) glass dosimeters

PURPOSE

This study aimed to determine a low-dose protocol for digital chest tomosynthesis (DTS).

METHODS

Five simulated nodules with a CT number of approximately 100 HU with size diameter of 3, 5, 8, 10, and 12 mm were inserted into an anthropomorphic chest phantom (N1 Lungman model), and then scanned by DTS system (Definium 8000) with varying tube voltage, copper filter thickness, and dose ratio. Three radiophotoluminescent (RPL) glass dosimeters, type GD-352 M with a dimension of 1.5 × 12 mm, were used to measure the entrance surface air kerma (ESAK) in each protocol. The effective dose (ED) was calculated using the recorded total dose-area-product (DAP). The signal-to-noise ratio (SNR) was determined for qualitative image quality evaluation. The image criteria and nodule detection capability were scored by two experienced radiologists. The selected low-dose protocol was further applied in a clinical study with 30 pulmonary nodule follow-up patients.

RESULTS

The average ESAK obtained from the standard default protocol was 1.68 ± 0.15 mGy, while an ESAK of 0.47 ± 0.02 mGy was found for a low-dose protocol. The EDs for the default and low-dose protocols were 313.98 ± 0.72 µSv and 100.55 ± 0.28 µSv, respectively. There were small non-significant differences in the image criteria and nodule detection scoring between the low-dose and default protocols interpreted by two radiologists. The effective dose of 98.87 ± 0.08 µSv was obtained in clinical study after applying the low-dose protocol.

CONCLUSIONS

The low-dose protocol obtained in this study can substantially reduce radiation dose while preserving an acceptable image quality compared to the standard protocol.

An adaptive pulmonary nodule detection algorithm

Recently, lung cancer has been paid more and more attention. People have reached a consensus that early detection and early treatment can improve the survival rate of patients. Among them, pulmonary nodules are the important reference for doctors to determine the lung health. With the continuous improvement of CT image resolution, more suspected pulmonary nodule information appears from the impact of chest CT. How to relatively and accurately locate the suspected nodule location from a large number of CT images has brought challenges to the doctor's daily diagnosis. To solve the problem that the original DBSCAN clustering algorithm needs manual setting of the threshold, this paper proposes a region growing algorithm and an adaptive DBSCAN clustering algorithm to improve the accuracy of pulmonary nodule detection. The image is roughly processed and ROI (Regions of Interest) region is roughly extracted by CLAHE transform. The region growing algorithm is used to roughly process the adjacent region's expansibility and the suspected region in ROI, and mark the center point in the region and the boundary point of its point set. The mean value of region range is taken as the threshold value of DBSCAN clustering algorithm. The center of the point domain is used as the starting point of clustering, and the rough set of points is used as the MinPts threshold. Finally, the clustering results are labeled in the initial CT image. Experiments show that the pulmonary nodule detection method proposed in this paper effectively improves the accuracy of the detection results.

Multi-scale gradual integration CNN for false positive reduction in pulmonary nodule detection

Lung cancer is a global and dangerous disease, and its early detection is crucial for reducing the risks of mortality. In this regard, it has been of great interest in developing a computer-aided system for pulmonary nodules detection as early as possible on thoracic CT scans. In general, a nodule detection system involves two steps: (i) candidate nodule detection at a high sensitivity, which captures many false positives and (ii) false positive reduction from candidates. However, due to the high variation of nodule morphological characteristics and the possibility of mistaking them for neighboring organs, candidate nodule detection remains a challenge. In this study, we propose a novel Multi-scale Gradual Integration Convolutional Neural Network (MGI-CNN), designed with three main strategies: (1) to use multi-scale inputs with different levels of contextual information, (2) to use abstract information inherent in different input scales with gradual integration, and (3) to learn multi-stream feature integration in an end-to-end manner. To verify the efficacy of the proposed network, we conducted exhaustive experiments on the LUNA16 challenge datasets by comparing the performance of the proposed method with state-of-the-art methods in the literature. On two candidate subsets of the LUNA16 dataset, i.e., V1 and V2, our method achieved an average CPM of 0.908 (V1) and 0.942 (V2), outperforming comparable methods by a large margin. Our MGI-CNN is implemented in Python using TensorFlow and the source code is available from https://github.com/ku-milab/MGICNN.

Single-view 2D CNNs with fully automatic non-nodule categorization for false positive reduction in pulmonary nodule detection

BACKGROUND AND OBJECTIVE

In pulmonary nodule detection, the first stage, candidate detection, aims to detect suspicious pulmonary nodules. However, detected candidates include many false positives and thus in the following stage, false positive reduction, such false positives are reliably reduced. Note that this task is challenging due to 1) the imbalance between the numbers of nodules and non-nodules and 2) the intra-class diversity of non-nodules. Although techniques using 3D convolutional neural networks (CNNs) have shown promising performance, they suffer from high computational complexity which hinders constructing deep networks. To efficiently address these problems, we propose a novel framework using the ensemble of 2D CNNs using single views, which outperforms existing 3D CNN-based methods.

METHODS

Our ensemble of 2D CNNs utilizes single-view 2D patches to improve both computational and memory efficiency compared to previous techniques exploiting 3D CNNs. We first categorize non-nodules on the basis of features encoded by an autoencoder. Then, all 2D CNNs are trained by using the same nodule samples, but with different types of non-nodules. By extending the learning capability, this training scheme resolves difficulties of extracting representative features from non-nodules with large appearance variations. Note that, instead of manual categorization requiring the heavy workload of radiologists, we propose to automatically categorize non-nodules based on the autoencoder and k-means clustering.

RESULTS

We performed extensive experiments to validate the effectiveness of our framework based on the database of the lung nodule analysis 2016 challenge. The superiority of our framework is demonstrated through comparing the performance of five frameworks trained with differently constructed training sets. Our proposed framework achieved state-of-the-art performance (0.922 of the competition performance metric score) with low computational demands (789K of parameters and 1024M of floating point operations per second).

CONCLUSION

We presented a novel false positive reduction framework, the ensemble of single-view 2D CNNs with fully automatic non-nodule categorization, for pulmonary nodule detection. Unlike previous 3D CNN-based frameworks, we utilized 2D CNNs using 2D single views to improve computational efficiency. Also, our training scheme using categorized non-nodules, extends the learning capability of representative features of different non-nodules. Our framework achieved state-of-the-art performance with low computational complexity.

An Assisted Diagnosis System for Detection of Early Pulmonary Nodule in Computed Tomography Images

Lung cancer is still the most concerned disease around the world. Lung nodule generates in the pulmonary parenchyma which indicates the latent risk of lung cancer. Computer-aided pulmonary nodules detection system is necessary, which can reduce diagnosis time and decrease mortality of patients. In this study, we have proposed a new computer aided diagnosis (CAD) system for detection of early pulmonary nodule, which can help radiologists quickly locate suspected nodules and make judgments. This system consists of four main sections: pulmonary parenchyma segmentation, nodule candidate detection, features extraction (total 22 features) and nodule classification. The publicly available data set created by the Lung Image Database Consortium (LIDC) is used for training and testing. This study selects 6400 slices from 80 CT scans containing totally 978 nodules, which is labeled by four radiologists. Through a fast segmentation method proposed in this paper, pulmonary nodules including 888 true nodules and 11,379 false positive nodules are segmented. By means of an ensemble classifier, Random Forest (RF), this study acquires 93.2, 92.4, 94.8, 97.6% of accuracy, sensitivity, specificity, area under the curve (AUC), respectively. Compared with support vector machine (SVM) classifier, RF can reduce more false positive nodules and acquire larger AUC. With the help of this CAD system, radiologist can be provided with a great reference for pulmonary nodule diagnosis timely.

Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor

Computer-aided detection (CAD) can help radiologists to detect pulmonary nodules at an early stage. In pulmonary nodule CAD systems, feature extraction is very important for describing the characteristics of nodule candidates. In this paper, we propose a novel three-dimensional shape-based feature descriptor to detect pulmonary nodules in CT scans. After lung volume segmentation, nodule candidates are detected using multi-scale dot enhancement filtering in the segmented lung volume. Next, we extract feature descriptors from the detected nodule candidates, and these are refined using an iterative wall elimination method. Finally, a support vector machine-based classifier is trained to classify nodules and non-nodules. The performance of the proposed system is evaluated on Lung Image Database Consortium data. The proposed method significantly reduces the number of false positives in nodule candidates. This method achieves 97.5% sensitivity, with only 6.76 false positives per scan.