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Jian M, Chen H, Zhang Z, Yang N, Zhang H, Ma L, Xu W, Zhi H. A Lung Nodule Dataset with Histopathology-based Cancer Type Annotation. Sci Data 2024; 11:824. [PMID: 39068171 PMCID: PMC11283520 DOI: 10.1038/s41597-024-03658-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 07/17/2024] [Indexed: 07/30/2024] Open
Abstract
Recently, Computer-Aided Diagnosis (CAD) systems have emerged as indispensable tools in clinical diagnostic workflows, significantly alleviating the burden on radiologists. Nevertheless, despite their integration into clinical settings, CAD systems encounter limitations. Specifically, while CAD systems can achieve high performance in the detection of lung nodules, they face challenges in accurately predicting multiple cancer types. This limitation can be attributed to the scarcity of publicly available datasets annotated with expert-level cancer type information. This research aims to bridge this gap by providing publicly accessible datasets and reliable tools for medical diagnosis, facilitating a finer categorization of different types of lung diseases so as to offer precise treatment recommendations. To achieve this objective, we curated a diverse dataset of lung Computed Tomography (CT) images, comprising 330 annotated nodules (nodules are labeled as bounding boxes) from 95 distinct patients. The quality of the dataset was evaluated using a variety of classical classification and detection models, and these promising results demonstrate that the dataset has a feasible application and further facilitate intelligent auxiliary diagnosis.
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Affiliation(s)
- Muwei Jian
- School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China.
- School of Information Science and Technology, Linyi University, Linyi, China.
| | - Hongyu Chen
- School of Information Science and Technology, Linyi University, Linyi, China
| | - Zaiyong Zhang
- Thoracic Surgery Department of Linyi Central Hospital, Linyi, China
| | - Nan Yang
- School of Information Science and Technology, Linyi University, Linyi, China
| | - Haorang Zhang
- School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China
| | - Lifu Ma
- Personnel Department of Linyi Central Hospital, Linyi, China
| | - Wenjing Xu
- School of Information Science and Technology, Linyi University, Linyi, China
| | - Huixiang Zhi
- School of Information Science and Technology, Linyi University, Linyi, China
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2
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Zhu K, Shen Z, Wang M, Jiang L, Zhang Y, Yang T, Zhang H, Zhang M. Visual Knowledge Domain of Artificial Intelligence in Computed Tomography: A Review Based on Bibliometric Analysis. J Comput Assist Tomogr 2024; 48:652-662. [PMID: 38271538 DOI: 10.1097/rct.0000000000001585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
ABSTRACT Artificial intelligence (AI)-assisted medical imaging technology is a new research area of great interest that has developed rapidly over the last decade. However, there has been no bibliometric analysis of published studies in this field. The present review focuses on AI-related studies on computed tomography imaging in the Web of Science database and uses CiteSpace and VOSviewer to generate a knowledge map and conduct the basic information analysis, co-word analysis, and co-citation analysis. A total of 7265 documents were included and the number of documents published had an overall upward trend. Scholars from the United States and China have made outstanding achievements, and there is a general lack of extensive cooperation in this field. In recent years, the research areas of great interest and difficulty have been the optimization and upgrading of algorithms, and the application of theoretical models to practical clinical applications. This review will help researchers understand the developments, research areas of great interest, and research frontiers in this field and provide reference and guidance for future studies.
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Zeng M, Wang X, Chen W. Worldwide research landscape of artificial intelligence in lung disease: A scientometric study. Heliyon 2024; 10:e31129. [PMID: 38826704 PMCID: PMC11141367 DOI: 10.1016/j.heliyon.2024.e31129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 05/09/2024] [Accepted: 05/10/2024] [Indexed: 06/04/2024] Open
Abstract
Purpose To perform a comprehensive bibliometric analysis of the application of artificial intelligence (AI) in lung disease to understand the current status and emerging trends of this field. Materials and methods AI-based lung disease research publications were selected from the Web of Science Core Collection. Citespace, VOS viewer and Excel were used to analyze and visualize co-authorship, co-citation, and co-occurrence analysis of authors, keywords, countries/regions, references and institutions in this field. Results Our study included a total of 5210 papers. The number of publications on AI in lung disease showed explosive growth since 2017. China and the United States lead in publication numbers. The most productive author were Li, Weimin and Qian Wei, with Shanghai Jiaotong University as the most productive institution. Radiology was the most co-cited journal. Lung cancer and COVID-19 emerged as the most studied diseases. Deep learning, convolutional neural network, lung cancer, radiomics will be the focus of future research. Conclusions AI-based diagnosis and treatment of lung disease has become a research hotspot in recent years, yielding significant results. Future work should focus on establishing multimodal AI models that incorporate clinical, imaging and laboratory information. Enhanced visualization of deep learning, AI-driven differential diagnosis model for lung disease and the creation of international large-scale lung disease databases should also be considered.
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Affiliation(s)
| | | | - Wei Chen
- Department of Radiology, Southwest Hospital, Third Military Medical University, Chongqing, China
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4
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Quanyang W, Yao H, Sicong W, Linlin Q, Zewei Z, Donghui H, Hongjia L, Shijun Z. Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis. Cancer Med 2024; 13:e7140. [PMID: 38581113 PMCID: PMC10997848 DOI: 10.1002/cam4.7140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 03/15/2024] [Accepted: 03/16/2024] [Indexed: 04/08/2024] Open
Abstract
BACKGROUND The exceptional capabilities of artificial intelligence (AI) in extracting image information and processing complex models have led to its recognition across various medical fields. With the continuous evolution of AI technologies based on deep learning, particularly the advent of convolutional neural networks (CNNs), AI presents an expanded horizon of applications in lung cancer screening, including lung segmentation, nodule detection, false-positive reduction, nodule classification, and prognosis. METHODOLOGY This review initially analyzes the current status of AI technologies. It then explores the applications of AI in lung cancer screening, including lung segmentation, nodule detection, and classification, and assesses the potential of AI in enhancing the sensitivity of nodule detection and reducing false-positive rates. Finally, it addresses the challenges and future directions of AI in lung cancer screening. RESULTS AI holds substantial prospects in lung cancer screening. It demonstrates significant potential in improving nodule detection sensitivity, reducing false-positive rates, and classifying nodules, while also showing value in predicting nodule growth and pathological/genetic typing. CONCLUSIONS AI offers a promising supportive approach to lung cancer screening, presenting considerable potential in enhancing nodule detection sensitivity, reducing false-positive rates, and classifying nodules. However, the universality and interpretability of AI results need further enhancement. Future research should focus on the large-scale validation of new deep learning-based algorithms and multi-center studies to improve the efficacy of AI in lung cancer screening.
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Affiliation(s)
- Wu Quanyang
- Department of Diagnostic RadiologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Huang Yao
- Department of Diagnostic RadiologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Wang Sicong
- Magnetic Resonance Imaging ResearchGeneral Electric Healthcare (China)BeijingChina
| | - Qi Linlin
- Department of Diagnostic RadiologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Zhang Zewei
- PET‐CT CenterNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Hou Donghui
- Department of Diagnostic RadiologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Li Hongjia
- PET‐CT CenterNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Zhao Shijun
- Department of Diagnostic RadiologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
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Ma L, Li G, Feng X, Fan Q, Liu L. TiCNet: Transformer in Convolutional Neural Network for Pulmonary Nodule Detection on CT Images. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:196-208. [PMID: 38343213 DOI: 10.1007/s10278-023-00904-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 07/19/2023] [Accepted: 08/10/2023] [Indexed: 03/02/2024]
Abstract
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.
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Affiliation(s)
- Ling Ma
- College of Software, Nankai University, Tianjin, China
| | - Gen Li
- College of Software, Nankai University, Tianjin, China
| | - Xingyu Feng
- College of Software, Nankai University, Tianjin, China
| | - Qiliang Fan
- College of Software, Nankai University, Tianjin, China
| | - Lizhi Liu
- Department of Radiology, Sun Yat-Sen University Cancer Center, Guangdong, China.
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Jian M, Jin H, Zhang L, Wei B, Yu H. DBPNDNet: dual-branch networks using 3DCNN toward pulmonary nodule detection. Med Biol Eng Comput 2024; 62:563-573. [PMID: 37945795 DOI: 10.1007/s11517-023-02957-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 10/21/2023] [Indexed: 11/12/2023]
Abstract
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.
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Affiliation(s)
- Muwei Jian
- School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China.
- School of Information Science and Technology, Linyi University, Linyi, China.
| | - Haodong Jin
- School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China
- School of Control Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Linsong Zhang
- School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China
| | - Benzheng Wei
- Medical Artificial Intelligence Research Center, Shandong University of Traditional Chinese Medicine, Qingdao, China
| | - Hui Yu
- School of Control Engineering, University of Shanghai for Science and Technology, Shanghai, China
- School of Creative Technologies, University of Portsmouth, Portsmouth, UK
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7
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Mao K, Jing X, Wang G, Chang Y, Liu J, Zhao Y, Yu S, Liu J. A novel open-source CADs platform for 3D CT pulmonary analysis. Comput Biol Med 2024; 169:107878. [PMID: 38141446 DOI: 10.1016/j.compbiomed.2023.107878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 11/10/2023] [Accepted: 12/18/2023] [Indexed: 12/25/2023]
Abstract
Computer-aided diagnosis (CAD) systems play vital roles in the early detection of pulmonary nodules for reducing lung cancer mortality rates. To provide better services for professional doctors, this paper proposes an efficient open-source CAD platform with flexible equipments, user-friendly interfaces, and completed functions for 3D CT pulmonary nodule analysis. For the platform's design and implementation, we fully consider application scenarios and system requirements. The platform supplies core functions for (1) Basic Image Processing, (2) Intelligent Image Analysis, (3) Multi-View Image Visualization, (4) Report Editing and Generation, (5) User Information Management, and (6) Inference Service Monitoring. Specifically, other state-of-the-art or user-defined algorithms can be integrated as plugin modules with no interference for system architecture. System evaluation with use-case testing demonstrates the effectiveness and universality of the proposed platform.
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Affiliation(s)
- Keming Mao
- Software College, Northeastern University, Shenyang, China
| | - Xin Jing
- Software College, Northeastern University, Shenyang, China
| | - Gao Wang
- Software College, Northeastern University, Shenyang, China
| | - Yachen Chang
- School of Software Technology, Zhejiang University, Ningbo, China
| | - Jiale Liu
- Software College, Northeastern University, Shenyang, China.
| | - Yuhai Zhao
- College of Computer Science and Engineering, Northeastern University, Shenyang, China
| | - Shiyu Yu
- China Mobile Group Liaoning Company Limited, Shenyang, China
| | - Jingyu Liu
- China Mobile Group Liaoning Company Limited, Shenyang, China
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8
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Zhang L, Shao Y, Chen G, Tian S, Zhang Q, Wu J, Bai C, Yang D. An artificial intelligence-assisted diagnostic system for the prediction of benignity and malignancy of pulmonary nodules and its practical value for patients with different clinical characteristics. Front Med (Lausanne) 2023; 10:1286433. [PMID: 38196835 PMCID: PMC10774219 DOI: 10.3389/fmed.2023.1286433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 12/12/2023] [Indexed: 01/11/2024] Open
Abstract
Objectives This study aimed to explore the value of an artificial intelligence (AI)-assisted diagnostic system in the prediction of pulmonary nodules. Methods The AI system was able to make predictions of benign or malignant nodules. 260 cases of solitary pulmonary nodules (SPNs) were divided into 173 malignant cases and 87 benign cases based on the surgical pathological diagnosis. A stratified data analysis was applied to compare the diagnostic effectiveness of the AI system to distinguish between the subgroups with different clinical characteristics. Results The accuracy of AI system in judging benignity and malignancy of the nodules was 75.77% (p < 0.05). We created an ROC curve by calculating the true positive rate (TPR) and the false positive rate (FPR) at different threshold values, and the AUC was 0.755. Results of the stratified analysis were as follows. (1) By nodule position: the AUC was 0.677, 0.758, 0.744, 0.982, and 0.725, respectively, for the nodules in the left upper lobe, left lower lobe, right upper lobe, right middle lobe, and right lower lobe. (2) By nodule size: the AUC was 0.778, 0.771, and 0.686, respectively, for the nodules measuring 5-10, 10-20, and 20-30 mm in diameter. (3) The predictive accuracy was higher for the subsolid pulmonary nodules than for the solid ones (80.54 vs. 66.67%). Conclusion The AI system can be applied to assist in the prediction of benign and malignant pulmonary nodules. It can provide a valuable reference, especially for the diagnosis of subsolid nodules and small nodules measuring 5-10 mm in diameter.
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Affiliation(s)
- Lichuan Zhang
- Department of Respiratory Medicine, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Yue Shao
- Department of Respiratory Medicine, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Guangmei Chen
- Department of Respiratory Medicine, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Simiao Tian
- Department of Respiratory Medicine, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Qing Zhang
- Department of Respiratory Medicine, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Jianlin Wu
- Department of Respiratory Medicine, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Chunxue Bai
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital Fudan University, Shanghai, China
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China
- Shanghai Respiratory Research Institution, Shanghai, China
| | - Dawei Yang
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital Fudan University, Shanghai, China
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China
- Shanghai Respiratory Research Institution, Shanghai, China
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Rai HM, Yoo J. A comprehensive analysis of recent advancements in cancer detection using machine learning and deep learning models for improved diagnostics. J Cancer Res Clin Oncol 2023; 149:14365-14408. [PMID: 37540254 DOI: 10.1007/s00432-023-05216-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 07/26/2023] [Indexed: 08/05/2023]
Abstract
PURPOSE There are millions of people who lose their life due to several types of fatal diseases. Cancer is one of the most fatal diseases which may be due to obesity, alcohol consumption, infections, ultraviolet radiation, smoking, and unhealthy lifestyles. Cancer is abnormal and uncontrolled tissue growth inside the body which may be spread to other body parts other than where it has originated. Hence it is very much required to diagnose the cancer at an early stage to provide correct and timely treatment. Also, manual diagnosis and diagnostic error may cause of the death of many patients hence much research are going on for the automatic and accurate detection of cancer at early stage. METHODS In this paper, we have done the comparative analysis of the diagnosis and recent advancement for the detection of various cancer types using traditional machine learning (ML) and deep learning (DL) models. In this study, we have included four types of cancers, brain, lung, skin, and breast and their detection using ML and DL techniques. In extensive review we have included a total of 130 pieces of literature among which 56 are of ML-based and 74 are from DL-based cancer detection techniques. Only the peer reviewed research papers published in the recent 5-year span (2018-2023) have been included for the analysis based on the parameters, year of publication, feature utilized, best model, dataset/images utilized, and best accuracy. We have reviewed ML and DL-based techniques for cancer detection separately and included accuracy as the performance evaluation metrics to maintain the homogeneity while verifying the classifier efficiency. RESULTS Among all the reviewed literatures, DL techniques achieved the highest accuracy of 100%, while ML techniques achieved 99.89%. The lowest accuracy achieved using DL and ML approaches were 70% and 75.48%, respectively. The difference in accuracy between the highest and lowest performing models is about 28.8% for skin cancer detection. In addition, the key findings, and challenges for each type of cancer detection using ML and DL techniques have been presented. The comparative analysis between the best performing and worst performing models, along with overall key findings and challenges, has been provided for future research purposes. Although the analysis is based on accuracy as the performance metric and various parameters, the results demonstrate a significant scope for improvement in classification efficiency. CONCLUSION The paper concludes that both ML and DL techniques hold promise in the early detection of various cancer types. However, the study identifies specific challenges that need to be addressed for the widespread implementation of these techniques in clinical settings. The presented results offer valuable guidance for future research in cancer detection, emphasizing the need for continued advancements in ML and DL-based approaches to improve diagnostic accuracy and ultimately save more lives.
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Affiliation(s)
- Hari Mohan Rai
- School of Computing, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si, 13120, Gyeonggi-do, Republic of Korea.
| | - Joon Yoo
- School of Computing, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si, 13120, Gyeonggi-do, Republic of Korea
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Wang X, Su R, Xie W, Wang W, Xu Y, Mann R, Han J, Tan T. 2.75D: Boosting learning by representing 3D Medical imaging to 2D features for small data. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2023]
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11
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Annavarapu CSR, Parisapogu SAB, Keetha NV, Donta PK, Rajita G. A Bi-FPN-Based Encoder-Decoder Model for Lung Nodule Image Segmentation. Diagnostics (Basel) 2023; 13:diagnostics13081406. [PMID: 37189507 DOI: 10.3390/diagnostics13081406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 04/02/2023] [Accepted: 04/04/2023] [Indexed: 05/17/2023] Open
Abstract
Early detection and analysis of lung cancer involve a precise and efficient lung nodule segmentation in computed tomography (CT) images. However, the anonymous shapes, visual features, and surroundings of the nodules as observed in the CT images pose a challenging and critical problem to the robust segmentation of lung nodules. This article proposes a resource-efficient model architecture: an end-to-end deep learning approach for lung nodule segmentation. It incorporates a Bi-FPN (bidirectional feature network) between an encoder and a decoder architecture. Furthermore, it uses the Mish activation function and class weights of masks with the aim of enhancing the efficiency of the segmentation. The proposed model was extensively trained and evaluated on the publicly available LUNA-16 dataset consisting of 1186 lung nodules. To increase the probability of the suitable class of each voxel in the mask, a weighted binary cross-entropy loss of each sample of training was utilized as network training parameter. Moreover, on the account of further evaluation of robustness, the proposed model was evaluated on the QIN Lung CT dataset. The results of the evaluation show that the proposed architecture outperforms existing deep learning models such as U-Net with a Dice Similarity Coefficient of 82.82% and 81.66% on both datasets.
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Affiliation(s)
| | | | - Nikhil Varma Keetha
- Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, India
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12
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Chen Y, Hou X, Yang Y, Ge Q, Zhou Y, Nie S. A Novel Deep Learning Model Based on Multi-Scale and Multi-View for Detection of Pulmonary Nodules. J Digit Imaging 2023; 36:688-699. [PMID: 36544067 PMCID: PMC10039158 DOI: 10.1007/s10278-022-00749-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 11/03/2022] [Accepted: 12/02/2022] [Indexed: 12/24/2022] Open
Abstract
Lung cancer manifests as pulmonary nodules in the early stage. Thus, the early and accurate detection of these nodules is crucial for improving the survival rate of patients. We propose a novel two-stage model for lung nodule detection. In the candidate nodule detection stage, a deep learning model based on 3D context information roughly segments the nodules detects the preprocessed image and obtain candidate nodules. In this model, 3D image blocks are input into the constructed model, and it learns the contextual information between the various slices in the 3D image block. The parameters of our model are equivalent to those of a 2D convolutional neural network (CNN), but the model could effectively learn the 3D context information of the nodules. In the false-positive reduction stage, we propose a multi-scale shared convolutional structure model. Our lung detection model has no significant increase in parameters and computation in both stages of multi-scale and multi-view detection. The proposed model was evaluated by using 888 computed tomography (CT) scans from the LIDC-IDRI dataset and achieved a competition performance metric (CPM) score of 0.957. The average detection sensitivity per scan was 0.971/1.0 FP. Furthermore, an average detection sensitivity of 0.933/1.0 FP per scan was achieved based on data from Shanghai Pulmonary Hospital. Our model exhibited a higher detection sensitivity, a lower false-positive rate, and better generalization than current lung nodule detection methods. The method has fewer parameters and less computational complexity, which provides more possibilities for the clinical application of this method.
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Affiliation(s)
- Yang Chen
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Xuewen Hou
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Yifeng Yang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Qianqian Ge
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Yan Zhou
- Department of Radiology, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, 200127, China.
| | - Shengdong Nie
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
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13
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Yuan H, Wu Y, Dai M. Multi-Modal Feature Fusion-Based Multi-Branch Classification Network for Pulmonary Nodule Malignancy Suspiciousness Diagnosis. J Digit Imaging 2023; 36:617-626. [PMID: 36478311 PMCID: PMC10039149 DOI: 10.1007/s10278-022-00747-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 09/28/2022] [Accepted: 11/27/2022] [Indexed: 12/13/2022] Open
Abstract
Detecting and identifying malignant nodules on chest computed tomography (CT) plays an important role in the early diagnosis and timely treatment of lung cancer, which can greatly reduce the number of deaths worldwide. In view of the existing methods in pulmonary nodule diagnosis, the importance of clinical radiological structured data (laboratory examination, radiological data) is ignored for the accuracy judgment of patients' condition. Hence, a multi-modal fusion multi-branch classification network is constructed to detect and classify pulmonary nodules in this work: (1) Radiological data of pulmonary nodules are used to construct structured features of length 9. (2) A multi-branch fusion-based effective attention mechanism network is designed for 3D CT Patch unstructured data, which uses 3D ECA-ResNet to dynamically adjust the extracted features. In addition, feature maps with different receptive fields from multi-layer are fully fused to obtain representative multi-scale unstructured features. (3) Multi-modal feature fusion of structured data and unstructured data is performed to distinguish benign and malignant nodules. Numerous experimental results show that this advanced network can effectively classify the benign and malignant pulmonary nodules for clinical diagnosis, which achieves the highest accuracy (94.89%), sensitivity (94.91%), and F1-score (94.65%) and lowest false positive rate (5.55%).
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Affiliation(s)
- Haiying Yuan
- Beijing University of Technology, Beijing, China.
| | - Yanrui Wu
- Beijing University of Technology, Beijing, China
| | - Mengfan Dai
- Beijing University of Technology, Beijing, China
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14
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Hao K, Cai A, Feng X, Ma L, Zhu J, Wang M, Zhang Y, Fei B. Lung nodule false positive reduction using a central attention convolutional neural network on imbalanced data. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2023; 12466:124661X. [PMID: 38487347 PMCID: PMC10940051 DOI: 10.1117/12.2654216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Abstract
Computer-aided detection systems for lung nodules play an important role in the early diagnosis and treatment process. False positive reduction is a significant component in pulmonary nodule detection. To address the visual similarities between nodules and false positives in CT images and the problem of two-class imbalanced learning, we propose a central attention convolutional neural network on imbalanced data (CACNNID) to distinguish nodules from a large number of false positive candidates. To solve the imbalanced data problem, we consider density distribution, data augmentation, noise reduction, and balanced sampling for making the network well-learned. During the network training, we design the model to pay high attention to the central information and minimize the influence of irrelevant edge information for extracting the discriminant features. The proposed model has been evaluated on the public dataset LUNA16 and achieved a mean sensitivity of 92.64%, specificity of 98.71%, accuracy of 98.69%, and AUC of 95.67%. The experimental results indicate that our model can achieve satisfactory performance in false positive reduction.
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Affiliation(s)
- Kexin Hao
- College of Software, Nankai University
| | - Annan Cai
- College of Software, Nankai University
| | | | - Ling Ma
- College of Software, Nankai University
| | | | | | - Yun Zhang
- Department of Radiology, Sun Yat-sen University Cancer Center
| | - Baowei Fei
- Department of Bioengineering, The University of Texas at Dallas
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15
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Lin J, She Q, Chen Y. Pulmonary nodule detection based on IR-UNet + + . Med Biol Eng Comput 2023; 61:485-495. [PMID: 36522521 DOI: 10.1007/s11517-022-02727-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Accepted: 12/06/2022] [Indexed: 12/23/2022]
Abstract
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.
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Affiliation(s)
- Jingchao Lin
- School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Qingshan She
- School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China.
| | - Yun Chen
- School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China.
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16
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de Margerie-Mellon C, Chassagnon G. Artificial intelligence: A critical review of applications for lung nodule and lung cancer. Diagn Interv Imaging 2023; 104:11-17. [PMID: 36513593 DOI: 10.1016/j.diii.2022.11.007] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 11/22/2022] [Indexed: 12/14/2022]
Abstract
Artificial intelligence (AI) is a broad concept that usually refers to computer programs that can learn from data and perform certain specific tasks. In the recent years, the growth of deep learning, a successful technique for computer vision tasks that does not require explicit programming, coupled with the availability of large imaging databases fostered the development of multiple applications in the medical imaging field, especially for lung nodules and lung cancer, mostly through convolutional neural networks (CNN). Some of the first applications of AI is this field were dedicated to automated detection of lung nodules on X-ray and computed tomography (CT) examinations, with performances now reaching or exceeding those of radiologists. For lung nodule segmentation, CNN-based algorithms applied to CT images show excellent spatial overlap index with manual segmentation, even for irregular and ground glass nodules. A third application of AI is the classification of lung nodules between malignant and benign, which could limit the number of follow-up CT examinations for less suspicious lesions. Several algorithms have demonstrated excellent capabilities for the prediction of the malignancy risk when a nodule is discovered. These different applications of AI for lung nodules are particularly appealing in the context of lung cancer screening. In the field of lung cancer, AI tools applied to lung imaging have been investigated for distinct aims. First, they could play a role for the non-invasive characterization of tumors, especially for histological subtype and somatic mutation predictions, with a potential therapeutic impact. Additionally, they could help predict the patient prognosis, in combination to clinical data. Despite these encouraging perspectives, clinical implementation of AI tools is only beginning because of the lack of generalizability of published studies, of an inner obscure working and because of limited data about the impact of such tools on the radiologists' decision and on the patient outcome. Radiologists must be active participants in the process of evaluating AI tools, as such tools could support their daily work and offer them more time for high added value tasks.
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Affiliation(s)
- Constance de Margerie-Mellon
- Université Paris Cité, Laboratory of Imaging Biomarkers, Center for Research on Inflammation, UMR 1149, INSERM, 75018 Paris, France; Department of Radiology, Hôpital Saint-Louis APHP, 75010 Paris, France
| | - Guillaume Chassagnon
- Université Paris Cité, Faculté de Médecine, 75006 Paris, France; Department of Radiology, Hôpital Cochin APHP, 75014 Paris, France
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17
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Liang X, Kong Y, Shang H, Yang M, Lu W, Zeng Q, Zhang G, Ye X. Computed tomography findings, associated factors, and management of pulmonary nodules in 54,326 healthy individuals. J Cancer Res Ther 2022; 18:2041-2048. [PMID: 36647968 DOI: 10.4103/jcrt.jcrt_1586_22] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Introduction To investigate the pulmonary nodules detected by low-dose computed tomography (LDCT), identified factors affecting the size and number of pulmonary nodules (single or multiple), and the pulmonary nodules diagnosed and management as lung cancer in healthy individuals. Methods A retrospective analysis was conducted on 54,326 healthy individuals who received chest LDCT screening. According to the results of screening, the detection rates of pulmonary nodules, grouped according to the size and number of pulmonary nodules (single or multiple), and the patients' gender, age, history of smoking, hypertension, and diabetes were statistically analyzed to determine the correlation between each factor and the characteristics of the nodules. The pulmonary nodules in healthy individuals diagnosed with lung cancer were managed with differently protocols. Results The detection rate of pulmonary nodules was 38.8% (21,055/54,326). The baseline demographic characteristics of patients with pulmonary nodules were: 58% male and 42% female patients, 25.7% smoking and 74.3% nonsmoking individuals, 40-60 years old accounted for 49%, 54.8% multiple nodules, and 45.2% single nodules, and ≤5-mm size accounted for 80.4%, 6-10 mm for 18.2%, and 11-30 mm for 1.4%. Multiple pulmonary nodules were more common in hypertensive patients. Diabetes is not an independent risk factor for several pulmonary nodules. Of all patients with lung nodules, 26 were diagnosed with lung cancer, accounting for 0.1% of all patients with pulmonary nodules, 0.6% with nodules ≥5 mm, and 2.2% with nodules ≥8 mm, respectively. Twenty-six patients with lung cancer were treated with surgical resection (57.7%), microwave ablation (MWA, 38.5%), and follow-up (3.8%). Conclusions LDCT was suitable for large-scale pulmonary nodules screening in healthy individuals, which was helpful for the early detection of suspicious lesions in the lung. In addition to surgical resection, MWA is an option for early lung cancer treatment.
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Affiliation(s)
- Xinyu Liang
- Department of Oncology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Shandong Lung Cancer Institute, No. 16766, Jingshi Road; Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong Province, China
| | - Yongmei Kong
- Shandong Second Provincial General Hospital, Jinan, Shandong Province, China
| | - Hui Shang
- Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong Province; Department of Radiology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Jinan, Shandong, China
| | - Mingxin Yang
- Department of Health Management, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, No. 16766, Jingshi Road, Jinan, Shandong Province, China
| | - Wenjing Lu
- Department of Oncology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Shandong Lung Cancer Institute, No. 16766, Jingshi Road; Shandong University of Traditional Chinese Medicine, Jinan, Shandong Province, China
| | - Qingshi Zeng
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Jinan, Shandong, China
| | - Guang Zhang
- Department of Health Management, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, No. 16766, Jingshi Road, Jinan, Shandong Province, China
| | - Xin Ye
- Department of Oncology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Shandong Lung Cancer Institute, No. 16766, Jingshi Road, Jinan, Shandong Province, China
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Sekeroglu K, Soysal ÖM. Multi-Perspective Hierarchical Deep-Fusion Learning Framework for Lung Nodule Classification. SENSORS (BASEL, SWITZERLAND) 2022; 22:8949. [PMID: 36433541 PMCID: PMC9697252 DOI: 10.3390/s22228949] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 11/09/2022] [Accepted: 11/10/2022] [Indexed: 06/16/2023]
Abstract
Lung cancer is the leading cancer type that causes mortality in both men and women. Computer-aided detection (CAD) and diagnosis systems can play a very important role for helping physicians with cancer treatments. This study proposes a hierarchical deep-fusion learning scheme in a CAD framework for the detection of nodules from computed tomography (CT) scans. In the proposed hierarchical approach, a decision is made at each level individually employing the decisions from the previous level. Further, individual decisions are computed for several perspectives of a volume of interest. This study explores three different approaches to obtain decisions in a hierarchical fashion. The first model utilizes raw images. The second model uses a single type of feature image having salient content. The last model employs multi-type feature images. All models learn the parameters by means of supervised learning. The proposed CAD frameworks are tested using lung CT scans from the LIDC/IDRI database. The experimental results showed that the proposed multi-perspective hierarchical fusion approach significantly improves the performance of the classification. The proposed hierarchical deep-fusion learning model achieved a sensitivity of 95% with only 0.4 fp/scan.
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Affiliation(s)
- Kazim Sekeroglu
- Department of Computer Science, Southeastern Louisiana University, Hammond, LA 70402, USA
| | - Ömer Muhammet Soysal
- Department of Computer Science, Southeastern Louisiana University, Hammond, LA 70402, USA
- School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, LA 70803, USA
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19
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An Automatic Random Walker Algorithm for Segmentation of Ground Glass Opacity Pulmonary Nodules. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:6727957. [PMID: 36212245 PMCID: PMC9537033 DOI: 10.1155/2022/6727957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 07/02/2021] [Accepted: 01/06/2022] [Indexed: 11/24/2022]
Abstract
Automatic and accurate segmentation of ground glass opacity (GGO) nodules still remains challenging due to inhomogeneous interiors, irregular shapes, and blurred boundaries from different patients. Despite successful applications in the image processing domains, the random walk has some limitations for segmentation of GGO pulmonary nodules. In this paper, an improved random walker method is proposed for the segmentation of GGO nodules. To calculate a new affinity matrix, intensity, spatial, and texture features are incorporated. It strengthens discriminative power between two adjacent nodes on the graph. To address the problem of robustness in seed acquisition, the geodesic distance is introduced and a novel local search strategy is presented to automatically acquire reliable seeds. For segmentation, a label constraint term is introduced to the energy function of original random walker, which alleviates the accumulation of errors caused by the initial seeds acquisition. Massive experiments conducted on Lung Images Dataset Consortium (LIDC) demonstrate that the proposed method achieves visually satisfactory results without user interactions. Both qualitative and quantitative evaluations also demonstrate that the proposed method obtains better performance compared with conventional random walker method and state-of-the-art segmentation methods in terms of the overlap score and F-measure.
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20
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Moragheb MA, Badie A, Noshad A. An Effective Approach for Automated Lung Node Detection using CT Scans. J Biomed Phys Eng 2022; 12:377-386. [PMID: 36059280 PMCID: PMC9395629 DOI: 10.31661/jbpe.v0i0.2110-1412] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 05/20/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Pulmonary or benign nodules are classified as nodules with a diameter of 3 cm or less and defined as non-cancerous nodules. The early diagnosis of malignant lung nodules is important for a more reliable prognosis of lung cancer and less invasive chemotherapy and radiotherapy procedures. OBJECTIVE This study aimed to introduce an improved hybrid approach for efficient nodule mask generation and false-positive reduction. MATERIAL AND METHODS In this experimental study, nodule segmentation preprocessing was conducted to prepare the input computed tomography (CT) scans for the U-Net convolutional neural network (CNN) model, and includes the normalization of CT scans and transfer of pixel values corresponding to the radiodensity of Hounsfield Units (HU). A U-Net CNN was developed based on lung CT scans for nodule identification. RESULTS The U-net model converged to a dice coefficient of 0.678 with a sensitivity of 75%. Many false positives were considered in every real positive, at 11.1, reduced in the proposed CNN to 2.32 FPs (False Positive) per TP (True Positive). CONCLUSION Based on the disadvantages of the largest nodule, the similarity of extracted features of the current study with those of others was imperative. The improved hybrid approach introduced was useful for other image classification tasks as expected.
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Affiliation(s)
- Mohammad Amin Moragheb
- MSc, Department of Computer Engineering, Faculty of Engineering, Mamasani Higher Education Center, Mamasani, Iran
| | - Ali Badie
- MSc, Department of Computer Engineering, Faculty of Engineering, Salman Farsi University of Kazerun, Kazerun, Iran
| | - Ali Noshad
- BSc, Department of Computer Engineering, Salman Farsi University of Kazerun, Kazerun, Iran
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21
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Mei J, Cheng MM, Xu G, Wan LR, Zhang H. SANet: A Slice-Aware Network for Pulmonary Nodule Detection. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022; 44:4374-4387. [PMID: 33687839 DOI: 10.1109/tpami.2021.3065086] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Lung cancer is the most common cause of cancer death worldwide. A timely diagnosis of the pulmonary nodules makes it possible to detect lung cancer in the early stage, and thoracic computed tomography (CT) provides a convenient way to diagnose nodules. However, it is hard even for experienced doctors to distinguish them from the massive CT slices. The currently existing nodule datasets are limited in both scale and category, which is insufficient and greatly restricts its applications. In this paper, we collect the largest and most diverse dataset named PN9 for pulmonary nodule detection by far. Specifically, it contains 8,798 CT scans and 40,439 annotated nodules from 9 common classes. We further propose a slice-aware network (SANet) for pulmonary nodule detection. A slice grouped non-local (SGNL) module is developed to capture long-range dependencies among any positions and any channels of one slice group in the feature map. And we introduce a 3D region proposal network to generate pulmonary nodule candidates with high sensitivity, while this detection stage usually comes with many false positives. Subsequently, a false positive reduction module (FPR) is proposed by using the multi-scale feature maps. To verify the performance of SANet and the significance of PN9, we perform extensive experiments compared with several state-of-the-art 2D CNN-based and 3D CNN-based detection methods. Promising evaluation results on PN9 prove the effectiveness of our proposed SANet. The dataset and source code is available at https://mmcheng.net/SANet/.
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22
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Astaraki M, Smedby Ö, Wang C. Prior-aware autoencoders for lung pathology segmentation. Med Image Anal 2022; 80:102491. [DOI: 10.1016/j.media.2022.102491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 04/11/2022] [Accepted: 05/20/2022] [Indexed: 10/18/2022]
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23
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Shu J, Wen D, Xu Z, Meng X, Zhang Z, Lin S, Zheng M. Improved interobserver agreement on nodule type and Lung-RADS classification of subsolid nodules using computer-aided solid component measurement. Eur J Radiol 2022; 152:110339. [DOI: 10.1016/j.ejrad.2022.110339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 04/06/2022] [Accepted: 05/01/2022] [Indexed: 11/16/2022]
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Hymecromone: a clinical prescription hyaluronan inhibitor for efficiently blocking COVID-19 progression. Signal Transduct Target Ther 2022; 7:91. [PMID: 35304437 PMCID: PMC8931182 DOI: 10.1038/s41392-022-00952-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 02/18/2022] [Accepted: 03/03/2022] [Indexed: 01/08/2023] Open
Abstract
Currently, there is no effective drugs for treating clinically COVID-19 except dexamethasone. We previously revealed that human identical sequences of SARS-CoV-2 promote the COVID-19 progression by upregulating hyaluronic acid (HA). As the inhibitor of HA synthesis, hymecromone is an approved prescription drug used for treating biliary spasm. Here, we aimed to investigate the relation between HA and COVID-19, and evaluate the therapeutic effects of hymecromone on COVID-19. Firstly, HA was closely relevant to clinical parameters, including lymphocytes (n = 158; r = −0.50; P < 0.0001), C-reactive protein (n = 156; r = 0.55; P < 0.0001), D-dimer (n = 154; r = 0.38; P < 0.0001), and fibrinogen (n = 152; r = 0.37; P < 0.0001), as well as the mass (n = 78; r = 0.43; P < 0.0001) and volume (n = 78; r = 0.41; P = 0.0002) of ground-glass opacity, the mass (n = 78; r = 0.48; P < 0.0001) and volume (n = 78; r = 0.47; P < 0.0001) of consolidation in patient with low level of hyaluronan (HA < 48.43 ng/mL). Furthermore, hyaluronan could directly cause mouse pulmonary lesions. Besides, hymecromone remarkably reduced HA via downregulating HAS2/HAS3 expression. Moreover, 89% patients with hymecromone treatment had pulmonary lesion absorption while only 42% patients in control group had pulmonary lesion absorption (P < 0.0001). In addition, lymphocytes recovered more quickly in hymecromone-treated patients (n = 8) than control group (n = 5) (P < 0.05). These findings suggest that hymecromone is a promising drug for COVID-19 and deserves our further efforts to determine its effect in a larger cohort.
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25
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Ouyang Z, Zhang P, Pan W, Li Q. Deep learning-based body part recognition algorithm for three-dimensional medical images. Med Phys 2022; 49:3067-3079. [PMID: 35157332 DOI: 10.1002/mp.15536] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 01/24/2022] [Accepted: 01/25/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND The automatic recognition of human body parts in three-dimensional (3D) medical images is important in many clinical applications. However, methods presented in prior studies have mainly classified each two-dimensional (2D) slice independently rather than recognizing a batch of consecutive slices as a specific body part. PURPOSE In this study, we aim to develop a deep-learning-based method designed to automatically divide computed tomography (CT) and magnetic resonance imaging (MRI) scans into five consecutive body parts: head, neck, chest, abdomen, and pelvis. METHODS A deep learning framework was developed to recognize body parts in two stages. In the first pre-classification stage, a convolutional neural network (CNN) using the GoogLeNet Inception v3 architecture and a long short-term memory (LSTM) network were combined to classify each 2D slice; the CNN extracted information from a single slice, whereas the LSTM employed rich contextual information among consecutive slices. In the second post-processing stage, the input scan was further partitioned into consecutive body parts by identifying the optimal boundaries between them based on the slice classification results of the first stage. To evaluate the performance of the proposed method, 662 CT and 1434 MRI scans were used. RESULTS Our method achieved a very good performance in 2D slice classification compared with state-of-the-art methods, with overall classification accuracies of 97.3% and 98.2% for CT and MRI scans, respectively. Moreover, our method further divided whole scans into consecutive body parts with mean boundary errors of 8.9 mm and 3.5 mm for CT and MRI data, respectively. CONCLUSIONS The proposed method significantly improved the slice classification accuracy compared with state-of-the-art methods, and further accurately divided CT and MRI scans into consecutive body parts based on the results of slice classification. The developed method can be employed as an important step in various computer-aided diagnosis and medical image analysis schemes. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Zihui Ouyang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Peng Zhang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Weifan Pan
- Zhejiang Taimei Medical Technology Co., Ltd, Jiaxing, Zhejiang, 314001, China
| | - Qiang Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
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26
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AFA: adversarial frequency alignment for domain generalized lung nodule detection. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-06928-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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27
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Lin FY, Chang YC, Huang HY, Li CC, Chen YC, Chen CM. A radiomics approach for lung nodule detection in thoracic CT images based on the dynamic patterns of morphological variation. Eur Radiol 2022; 32:3767-3777. [PMID: 35020016 DOI: 10.1007/s00330-021-08456-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Revised: 09/20/2021] [Accepted: 11/02/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVES To propose and evaluate a set of radiomic features, called morphological dynamics features, for pulmonary nodule detection, which were rooted in the dynamic patterns of morphological variation and needless precise lesion segmentation. MATERIALS AND METHODS Two datasets were involved, namely, university hospital (UH) and LIDC datasets, comprising 72 CT scans (360 nodules) and 888 CT scans (2230 nodules), respectively. Each nodule was annotated by multiple radiologists. Denoted the category of nodules identified by at least k radiologists as ALk. A nodule detection algorithm, called CAD-MD algorithm, was proposed based on the morphological dynamics radiomic features, characterizing a lesion by ten sets of the same features with different values extracted from ten different thresholding results. Each nodule candidate was classified by a two-level classifier, including ten decision trees and a random forest, respectively. The CAD-MD algorithm was compared with a deep learning approach, the N-Net, using the UH dataset. RESULTS On the AL1 and AL2 of the UH dataset, the AUC of the AFROC curves were 0.777 and 0.851 for the CAD-MD algorithm and 0.478 and 0.472 for the N-Net, respectively. The CAD-MD algorithm achieved the sensitivities of 84.4% and 91.4% with 2.98 and 3.69 FPs/scan and the N-Net 74.4% and 80.7% with 3.90 and 4.49 FPs/scan, respectively. On the LIDC dataset, the CAD-MD algorithm attained the sensitivities of 87.6%, 89.2%, 92.2%, and 95.0% with 4 FPs/scan for AL1-AL4, respectively. CONCLUSION The morphological dynamics radiomic features might serve as an effective set of radiomic features for lung nodule detection. KEY POINTS • Texture features varied with such CT system settings as reconstruction kernels of CT images, CT scanner models, and parameter settings, and so on. • Shape and first-order statistics were shown to be the most robust features against variation in CT imaging parameters. • The morphological dynamics radiomic features, which mainly characterized the dynamic patterns of morphological variation, were shown to be effective for lung nodule detection.
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Affiliation(s)
- Fan-Ya Lin
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 1, Jen-Ai Road, Taipei, 100, Taiwan
| | - Yeun-Chung Chang
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | | | - Chia-Chen Li
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 1, Jen-Ai Road, Taipei, 100, Taiwan
| | - Yi-Chang Chen
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 1, Jen-Ai Road, Taipei, 100, Taiwan.,Department of Medical Imaging, Cardinal Tien Hospital, New Taipei City, Taiwan
| | - Chung-Ming Chen
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 1, Jen-Ai Road, Taipei, 100, Taiwan.
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28
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Jiang W, Zeng G, Wang S, Wu X, Xu C. Application of Deep Learning in Lung Cancer Imaging Diagnosis. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:6107940. [PMID: 35028122 PMCID: PMC8749371 DOI: 10.1155/2022/6107940] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 10/24/2021] [Accepted: 11/05/2021] [Indexed: 11/17/2022]
Abstract
Lung cancer is one of the malignant tumors with the highest fatality rate and nearest to our lives. It poses a great threat to human health and it mainly occurs in smokers. In our country, with the acceleration of industrialization, environmental pollution, and population aging, the cancer burden of lung cancer is increasing day by day. In the diagnosis of lung cancer, Computed Tomography (CT) images are a fairly common visualization tool. CT images visualize all tissues based on the absorption of X-rays. The diseased parts of the lung are collectively referred to as pulmonary nodules, the shape of nodules is different, and the risk of cancer will vary with the shape of nodules. Computer-aided diagnosis (CAD) is a very suitable method to solve this problem because the computer vision model can quickly scan every part of the CT image of the same quality for analysis and will not be affected by fatigue and emotion. The latest advances in deep learning enable computer vision models to help doctors diagnose various diseases, and in some cases, models have shown greater competitiveness than doctors. Based on the opportunity of technological development, the application of computer vision in medical imaging diagnosis of diseases has important research significance and value. In this paper, we have used a deep learning-based model on CT images of lung cancer and verified its effectiveness in the timely and accurate prediction of lungs disease. The proposed model has three parts: (i) detection of lung nodules, (ii) False Positive Reduction of the detected nodules to filter out "false nodules," and (iii) classification of benign and malignant lung nodules. Furthermore, different network structures and loss functions were designed and realized at different stages. Additionally, to fine-tune the proposed deep learning-based mode and improve its accuracy in the detection Lung Nodule Detection, Noudule-Net, which is a detection network structure that combines U-Net and RPN, is proposed. Experimental observations have verified that the proposed scheme has exceptionally improved the expected accuracy and precision ratio of the underlined disease.
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Affiliation(s)
- Wenfa Jiang
- Thoracic Surgery Department, GanZhou People's Hospital, Ganzhou 341000, China
| | - Ganhua Zeng
- Thoracic Surgery Department, GanZhou People's Hospital, Ganzhou 341000, China
| | - Shuo Wang
- Ward 1, Ganzhou Cancer Hospital, Ganzhou, Jiangxi 341500, China
| | - Xiaofeng Wu
- The Three Departments of Medicine, Dayu County Peoples Hospital, Ganzhou, Jiangxi 341500, China
| | - Chenyang Xu
- Thoracic Surgery Department, GanZhou People's Hospital, Ganzhou 341000, China
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Ben-Zikri YK, Helguera M, Fetzer D, Shrier DA, Aylward SR, Chittajallu D, Niethammer M, Cahill ND, Linte CA. A Feature-based Affine Registration Method for Capturing Background Lung Tissue Deformation for Ground Glass Nodule Tracking. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING. IMAGING & VISUALIZATION 2022; 10:521-539. [PMID: 36465979 PMCID: PMC9718421 DOI: 10.1080/21681163.2021.1994471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Lung nodule tracking assessment relies on cross-sectional measurements of the largest lesion profile depicted in initial and follow-up computed tomography (CT) images. However, apparent changes in nodule size assessed via simple image-based measurements may also be compromised by the effect of the background lung tissue deformation on the GGN between the initial and follow-up images, leading to erroneous conclusions about nodule changes due to disease. To compensate for the lung deformation and enable consistent nodule tracking, here we propose a feature-based affine registration method and study its performance vis-a-vis several other registration methods. We implement and test each registration method using both a lung- and a lesion-centered region of interest on ten patient CT datasets featuring twelve nodules, including both benign and malignant GGO lesions containing pure GGNs, part-solid, or solid nodules. We evaluate each registration method according to the target registration error (TRE) computed across 30 - 50 homologous fiducial landmarks surrounding the lesions and selected by expert radiologists in both the initial and follow-up patient CT images. Our results show that the proposed feature-based affine lesion-centered registration yielded a 1.1 ± 1.2 mm TRE, while a Symmetric Normalization deformable registration yielded a 1.2 ± 1.2 mm TRE, and a least-square fit registration of the 30-50 validation fiducial landmark set yielded a 1.5 ± 1.2 mm TRE. Although the deformable registration yielded a slightly higher registration accuracy than the feature-based affine registration, it is significantly more computationally efficient, eliminates the need for ambiguous segmentation of GGNs featuring ill-defined borders, and reduces the susceptibility of artificial deformations introduced by the deformable registration, which may lead to increased similarity between the registered initial and follow-up images, over-compensating for the background lung tissue deformation, and, in turn, compromising the true disease-induced nodule change assessment. We also assessed the registration qualitatively, by visual inspection of the subtraction images, and conducted a pilot pre-clinical study that showed the proposed feature-based lesion-centered affine registration effectively compensates for the background lung tissue deformation between the initial and follow-up images and also serves as a reliable baseline registration method prior to assessing lung nodule changes due to disease.
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Affiliation(s)
- Yehuda K. Ben-Zikri
- Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA
| | - María Helguera
- Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA,Instituto Tecnológico José Mario Molina Pasquel y Henríquez, UnidadLagosdeM oreno, Jalisco, Mexico
| | - David Fetzer
- Dept. of Radiology, UT Southwestern Medical Center, Dallas, TX, USA
| | - David A. Shrier
- Dept. of Radiology, University of Rochester Medical Center, Rochester, NY, USA
| | | | | | - Marc Niethammer
- Dept. of Computer Science, University of North Carolina, Chapel Hill, NC, USA
| | - Nathan D. Cahill
- School of Mathematical Sciences, Rochester Institute of Technology, Rochester, NY, USA
| | - Cristian A. Linte
- Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA,Dept. of Biomedical Engineering, Rochester Institute of Technology, Rochester, NY, USA,Corresponding author.
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Bhatt SD, Soni HB. Improving Classification Accuracy of Pulmonary Nodules using Simplified Deep Neural Network. Open Biomed Eng J 2021. [DOI: 10.2174/1874120702115010180] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Background:
Lung cancer is among the major causes of death in the world. Early detection of lung cancer is a major challenge. These encouraged the development of Computer-Aided Detection (CAD) system.
Objectives:
We designed a CAD system for performance improvement in detecting and classifying pulmonary nodules. Though the system will not replace radiologists, it will be helpful to them in order to accurately diagnose lung cancer.
Methods:
The architecture comprises of two steps, among which in the first step CT scans are pre-processed and the candidates are extracted using the positive and negative annotations provided along with the LUNA16 dataset, and the second step consists of three different neural networks for classifying the pulmonary nodules obtained from the first step. The models in the second step consist of 2D-Convolutional Neural Network (2D-CNN), Visual Geometry Group-16 (VGG-16) and simplified VGG-16, which independently classify pulmonary nodules.
Results:
The classification accuracies achieved for 2D-CNN, VGG-16 and simplified VGG-16 were 99.12%, 98.17% and 99.60%, respectively.
Conclusion:
The integration of deep learning techniques along with machine learning and image processing can serve as a good means of extracting pulmonary nodules and classifying them with improved accuracy. Based on these results, it can be concluded that the transfer learning concept will improve system performance. In addition, performance improves proper designing of the CAD system by considering the amount of dataset and the availability of computing power.
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Guo Z, Zhao L, Yuan J, Yu H. MSANet Multi-Scale Aggregation Network Integrating Spatial and Channel Information for Lung Nodule Detection. IEEE J Biomed Health Inform 2021; 26:2547-2558. [PMID: 34847048 DOI: 10.1109/jbhi.2021.3131671] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
AbstractImproving the detection accuracy of pulmonary nodules plays an important role in the diagnosis and early treatment of lung cancer. In this paper, a multiscale aggregation network (MSANet), which integrates spatial and channel information, is proposed for 3D pulmonary nodule detection. MSANet is designed to improve the network's ability to extract information and realize multiscale information fusion. First, multiscale aggregation interaction strategies are used to extract multilevel features and avoid feature fusion interference caused by large resolution differences. These strategies can effectively integrate the contextual information of adjacent resolutions and help to detect different sized nodules. Second, the feature extraction module is designed for efficient channel attention and self-calibrated convolutions (ECA-SC) to enhance the interchannel and local spatial information. ECA-SC also recalibrates the features in the feature extraction process, which can realize adaptive learning of feature weights and enhance the information extraction ability of features. Third, the distribution ranking (DR) loss is introduced as the classification loss function to solve the problem of imbalanced data between positive and negative samples. The proposed MSANet is comprehensively compared with other pulmonary nodule detection networks on the LUNA16 dataset, and a CPM score of 0.920 is obtained. The results show that the sensitivity for detecting pulmonary nodules is improved and that the average number of false-positives is effectively reduced. The proposed method has advantages in pulmonary nodule detection and can effectively assist radiologists in pulmonary nodule detection.
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Yu H, Li J, Zhang L, Cao Y, Yu X, Sun J. Design of lung nodules segmentation and recognition algorithm based on deep learning. BMC Bioinformatics 2021; 22:314. [PMID: 34749636 PMCID: PMC8576909 DOI: 10.1186/s12859-021-04234-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 06/04/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Accurate segmentation and recognition algorithm of lung nodules has great important value of reference for early diagnosis of lung cancer. An algorithm is proposed for 3D CT sequence images in this paper based on 3D Res U-Net segmentation network and 3D ResNet50 classification network. The common convolutional layers in encoding and decoding paths of U-Net are replaced by residual units while the loss function is changed to Dice loss after using cross entropy loss to accelerate network convergence. Since the lung nodules are small and rich in 3D information, the ResNet50 is improved by replacing the 2D convolutional layers with 3D convolutional layers and reducing the sizes of some convolution kernels, 3D ResNet50 network is obtained for the diagnosis of benign and malignant lung nodules. RESULTS 3D Res U-Net was trained and tested on 1044 CT subcases in the LIDC-IDRI database. The segmentation result shows that the Dice coefficient of 3D Res U-Net is above 0.8 for the segmentation of lung nodules larger than 10 mm in diameter. 3D ResNet50 was trained and tested on 2960 lung nodules in the LIDC-IDRI database. The classification result shows that the diagnostic accuracy of 3D ResNet50 is 87.3% and AUC is 0.907. CONCLUSION The 3D Res U-Net module improves segmentation performance significantly with the comparison of 3D U-Net model based on residual learning mechanism. 3D Res U-Net can identify small nodules more effectively and improve its segmentation accuracy for large nodules. Compared with the original network, the classification performance of 3D ResNet50 is significantly improved, especially for small benign nodules.
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Affiliation(s)
- Hui Yu
- Department of Biomedical Engineering, Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, Tianjin University, Tianjin, China
| | - Jinqiu Li
- Department of Biomedical Engineering, Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, Tianjin University, Tianjin, China
| | - Lixin Zhang
- Department of Biomedical Engineering, Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, Tianjin University, Tianjin, China
| | - Yuzhen Cao
- Department of Biomedical Engineering, Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, Tianjin University, Tianjin, China
| | - Xuyao Yu
- Department of Radiotherapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Jinglai Sun
- Department of Biomedical Engineering, Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, Tianjin University, Tianjin, China
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Fu Y, Xue P, Li N, Zhao P, Xu Z, Ji H, Zhang Z, Cui W, Dong E. Fusion of 3D lung CT and serum biomarkers for diagnosis of multiple pathological types on pulmonary nodules. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 210:106381. [PMID: 34496322 DOI: 10.1016/j.cmpb.2021.106381] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 08/24/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Current researches on pulmonary nodules mainly focused on the binary-classification of benign and malignant pulmonary nodules. However, in clinical applications, it is not enough to judge whether pulmonary nodules are benign or malignant. In this paper, we proposed a fusion model based on the Lung Information Dataset Containing 3D CT Images and Serum Biomarkers (LIDCCISB) we constructed to accurately diagnose the types of pulmonary nodules in squamous cell carcinoma, adenocarcinoma, inflammation and other benign diseases. METHODS Using single modal information of lung 3D CT images and single modal information of Lung Tumor Biomarkers (LTBs) in LIDCCISB, a Multi-resolution 3D Multi-classification deep learning model (Mr-Mc) and a Multi-Layer Perceptron machine learning model (MLP) were constructed for diagnosing multiple pathological types of pulmonary nodules, respectively. To comprehensively use the double modal information of CT images and LTBs, we used transfer learning to fuse Mr-Mc and MLP, and constructed a multimodal information fusion model that could classify multiple pathological types of benign and malignant pulmonary nodules. RESULTS Experiments showed that the constructed Mr-Mc model can achieve an average accuracy of 0.805 and MLP model can achieve an average accuracy of 0.887. The fusion model was verified on a dataset containing 64 samples, and achieved an average accuracy of 0.906. CONCLUSIONS This is the first study to simultaneously use CT images and LTBs to diagnose multiple pathological types of benign and malignant pulmonary nodules, and experiments showed that our research was more advanced and more suitable for practical clinical applications.
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Affiliation(s)
- Yu Fu
- School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai 264209, China
| | - Peng Xue
- School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai 264209, China
| | - Ning Li
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China
| | - Peng Zhao
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China
| | - Zhuodong Xu
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China
| | - Huizhong Ji
- School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai 264209, China
| | - Zhili Zhang
- School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai 264209, China
| | - Wentao Cui
- School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai 264209, China.
| | - Enqing Dong
- School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai 264209, China.
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Wu F, Chen L, Huang J, Fan W, Yang J, Zhang X, Jin Y, Yang F, Zheng C. Total Lung and Lobar Quantitative Assessment Based on Paired Inspiratory-Expiratory Chest CT in Healthy Adults: Correlation with Pulmonary Ventilatory Function. Diagnostics (Basel) 2021; 11:diagnostics11101791. [PMID: 34679488 PMCID: PMC8534441 DOI: 10.3390/diagnostics11101791] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 09/21/2021] [Accepted: 09/24/2021] [Indexed: 11/16/2022] Open
Abstract
Objective: To provide the quantitative volumetric data of the total lung and lobes in inspiration and expiration from healthy adults, and to explore the value of paired inspiratory–expiratory chest CT scan in pulmonary ventilatory function and further explore the influence of each lobe on ventilation. Methods: A total of 65 adults (29 males and 36 females) with normal clinical pulmonary function test (PFT) and paired inspiratory–expiratory chest CT scan were retrospectively enrolled. The inspiratory and expiratory volumetric indexes of the total lung (TL) and 5 lobes (left upper lobe [LUL], left lower lobe [LLL], right upper lobe [RUL], right middle lobe [RML], and right lower lobe [RLL]) were obtained by Philips IntelliSpace Portal image postprocessing workstation, including inspiratory lung volume (LVin), expiratory lung volume (LVex), volume change (∆LV), and well-aerated lung volume (WAL, lung tissue with CT threshold between −950 and −750 HU in inspiratory scan). Spearman correlation analysis was used to explore the correlation between CT quantitative indexes of the total lung and ventilatory function indexes (including total lung capacity [TLC], residual volume [RV], and force vital capacity [FVC]). Multiple stepwise regression analysis was used to explore the influence of each lobe on ventilation. Results: At end-inspiratory phase, the LVin-TL was 4664.6 (4282.7, 5916.2) mL, the WALTL was 4173 (3639.6, 5250.9) mL; both showed excellent correlation with TLC (LVin-TL: r = 0.890, p < 0.001; WALTL: r = 0.879, p < 0.001). From multiple linear regression analysis with lobar CT indexes as variables, the LVin and WAL of these two lobes, LLL and RUL, showed a significant relationship with TLC. At end-expiratory phase, the LVex-TL was 2325.2 (1969.7, 2722.5) mL with good correlation with RV (r = 0.811, p < 0.001), of which the LVex of RUL and RML had a significant relationship with RV. For the volumetric change within breathing, the ∆LVTL was 2485.6 (2169.8, 3078.1) mL with good correlation with FVC (r = 0.719, p < 0.001), moreover, WALTL showed a better correlation with FVC (r = 0.817, p < 0.001) than that of ∆LVTL. Likewise, there was also a strong association between ∆LV, WAL of these two lobes (LLL and RUL), and FVC. Conclusions: The quantitative indexes derived from paired inspiratory–expiratory chest CT could reflect the clinical pulmonary ventilatory function, LLL, and RUL give greater impact on ventilation. Thus, the pulmonary functional evaluation needs to be more precise and not limited to the total lung level.
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Affiliation(s)
- Feihong Wu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Rd., Wuhan 430022, China; (F.W.); (L.C.); (J.H.); (W.F.); (J.Y.)
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Leqing Chen
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Rd., Wuhan 430022, China; (F.W.); (L.C.); (J.H.); (W.F.); (J.Y.)
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Jia Huang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Rd., Wuhan 430022, China; (F.W.); (L.C.); (J.H.); (W.F.); (J.Y.)
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Wenliang Fan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Rd., Wuhan 430022, China; (F.W.); (L.C.); (J.H.); (W.F.); (J.Y.)
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Jinrong Yang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Rd., Wuhan 430022, China; (F.W.); (L.C.); (J.H.); (W.F.); (J.Y.)
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Xiaohui Zhang
- Clinical Science, Philips Healthcare, No. 718 Daning Rd., Jingan District, Shanghai 200233, China;
| | - Yang Jin
- Department of Respiratory and Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Rd., Wuhan 430022, China;
| | - Fan Yang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Rd., Wuhan 430022, China; (F.W.); (L.C.); (J.H.); (W.F.); (J.Y.)
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
- Correspondence: (F.Y.); (C.Z.); Tel.: +86-027-8535-3238 (C.Z.)
| | - Chuansheng Zheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Rd., Wuhan 430022, China; (F.W.); (L.C.); (J.H.); (W.F.); (J.Y.)
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
- Correspondence: (F.Y.); (C.Z.); Tel.: +86-027-8535-3238 (C.Z.)
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Quantitative analysis of metastatic breast cancer in mice using deep learning on cryo-image data. Sci Rep 2021; 11:17527. [PMID: 34471169 PMCID: PMC8410829 DOI: 10.1038/s41598-021-96838-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 08/17/2021] [Indexed: 11/30/2022] Open
Abstract
Cryo-imaging sections and images a whole mouse and provides ~ 120-GBytes of microscopic 3D color anatomy and fluorescence images, making fully manual analysis of metastases an onerous task. A convolutional neural network (CNN)-based metastases segmentation algorithm included three steps: candidate segmentation, candidate classification, and semi-automatic correction of the classification result. The candidate segmentation generated > 5000 candidates in each of the breast cancer-bearing mice. Random forest classifier with multi-scale CNN features and hand-crafted intensity and morphology features achieved 0.8645 ± 0.0858, 0.9738 ± 0.0074, and 0.9709 ± 0.0182 sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic (ROC), with fourfold cross validation. Classification results guided manual correction by an expert with our in-house MATLAB software. Finally, 225, 148, 165, and 344 metastases were identified in the four cancer mice. With CNN-based segmentation, the human intervention time was reduced from > 12 to ~ 2 h. We demonstrated that 4T1 breast cancer metastases spread to the lung, liver, bone, and brain. Assessing the size and distribution of metastases proves the usefulness and robustness of cryo-imaging and our software for evaluating new cancer imaging and therapeutics technologies. Application of the method with only minor modification to a pancreatic metastatic cancer model demonstrated generalizability to other tumor models.
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Yuan H, Fan Z, Wu Y, Cheng J. An efficient multi-path 3D convolutional neural network for false-positive reduction of pulmonary nodule detection. Int J Comput Assist Radiol Surg 2021; 16:2269-2277. [PMID: 34449037 DOI: 10.1007/s11548-021-02478-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 08/10/2021] [Indexed: 12/19/2022]
Abstract
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.
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Affiliation(s)
- Haiying Yuan
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, People's Republic of China.
| | - Zhongwei Fan
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, People's Republic of China
| | - Yanrui Wu
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, People's Republic of China
| | - Junpeng Cheng
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, People's Republic of China
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Zuo W, Zhou F, He Y. An Embedded Multi-branch 3D Convolution Neural Network for False Positive Reduction in Lung Nodule Detection. J Digit Imaging 2021; 33:846-857. [PMID: 32095944 DOI: 10.1007/s10278-020-00326-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Numerous lung nodule candidates can be produced through an automated lung nodule detection system. Classifying these candidates to reduce false positives is an important step in the detection process. The objective during this paper is to predict real nodules from a large number of pulmonary nodule candidates. Facing the challenge of the classification task, we propose a novel 3D convolution neural network (CNN) to reduce false positives in lung nodule detection. The novel 3D CNN includes embedded multiple branches in its structure. Each branch processes a feature map from a layer with different depths. All of these branches are cascaded at their ends; thus, features from different depth layers are combined to predict the categories of candidates. The proposed method obtains a competitive score in lung nodule candidate classification on LUNA16 dataset with an accuracy of 0.9783, a sensitivity of 0.8771, a precision of 0.9426, and a specificity of 0.9925. Moreover, a good performance on the competition performance metric (CPM) is also obtained with a score of 0.830. As a 3D CNN, the proposed model can learn complete and three-dimensional discriminative information about nodules and non-nodules to avoid some misidentification problems caused due to lack of spatial correlation information extracted from traditional methods or 2D networks. As an embedded multi-branch structure, the model is also more effective in recognizing the nodules of various shapes and sizes. As a result, the proposed method gains a competitive score on the false positive reduction in lung nodule detection and can be used as a reference for classifying nodule candidates.
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Affiliation(s)
- Wangxia Zuo
- The School of Instrumentation and Optoelectronics Engineering, Beihang University, 37 Xueyuan Road, Haidian District, Beijing, 100083, China.,The College of Electrical Engineering, University of South China, Hengyang, 421001, Hunan, China
| | - Fuqiang Zhou
- The School of Instrumentation and Optoelectronics Engineering, Beihang University, 37 Xueyuan Road, Haidian District, Beijing, 100083, China.
| | - Yuzhu He
- The School of Instrumentation and Optoelectronics Engineering, Beihang University, 37 Xueyuan Road, Haidian District, Beijing, 100083, China
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Fast fully automatic detection, classification and 3D reconstruction of pulmonary nodules in CT images by local image feature analysis. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102790] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Farhangi MM, Sahiner B, Petrick N, Pezeshk A. Automatic lung nodule detection in thoracic CT scans using dilated slice-wise convolutions. Med Phys 2021; 48:3741-3751. [PMID: 33932241 DOI: 10.1002/mp.14915] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 04/08/2021] [Accepted: 04/15/2021] [Indexed: 12/24/2022] Open
Abstract
PURPOSE Most state-of-the-art automated medical image analysis methods for volumetric data rely on adaptations of two-dimensional (2D) and three-dimensional (3D) convolutional neural networks (CNNs). In this paper, we develop a novel unified CNN-based model that combines the benefits of 2D and 3D networks for analyzing volumetric medical images. METHODS In our proposed framework, multiscale contextual information is first extracted from 2D slices inside a volume of interest (VOI). This is followed by dilated 1D convolutions across slices to aggregate in-plane features in a slice-wise manner and encode the information in the entire volume. Moreover, we formalize a curriculum learning strategy for a two-stage system (i.e., a system that consists of screening and false positive reduction), where the training samples are presented to the network in a meaningful order to further improve the performance. RESULTS We evaluated the proposed approach by developing a computer-aided detection (CADe) system for lung nodules. Our results on 888 CT exams demonstrate that the proposed approach can effectively analyze volumetric data by achieving a sensitivity of > 0.99 in the screening stage and a sensitivity of > 0.96 at eight false positives per case in the false positive reduction stage. CONCLUSION Our experimental results show that the proposed method provides competitive results compared to state-of-the-art 3D frameworks. In addition, we illustrate the benefits of curriculum learning strategies in two-stage systems that are of common use in medical imaging applications.
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Affiliation(s)
- M Mehdi Farhangi
- Division of Imaging, Diagnostics, and Software Reliability, CDRH, U.S Food and Drug Administration, Silver Spring, MD, 20993, USA
| | - Berkman Sahiner
- Division of Imaging, Diagnostics, and Software Reliability, CDRH, U.S Food and Drug Administration, Silver Spring, MD, 20993, USA
| | - Nicholas Petrick
- Division of Imaging, Diagnostics, and Software Reliability, CDRH, U.S Food and Drug Administration, Silver Spring, MD, 20993, USA
| | - Aria Pezeshk
- Division of Imaging, Diagnostics, and Software Reliability, CDRH, U.S Food and Drug Administration, Silver Spring, MD, 20993, USA
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Shan F, Gao Y, Wang J, Shi W, Shi N, Han M, Xue Z, Shen D, Shi Y. Abnormal lung quantification in chest CT images of COVID-19 patients with deep learning and its application to severity prediction. Med Phys 2021; 48:1633-1645. [PMID: 33225476 PMCID: PMC7753662 DOI: 10.1002/mp.14609] [Citation(s) in RCA: 93] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 09/08/2020] [Accepted: 10/28/2020] [Indexed: 12/20/2022] Open
Abstract
OBJECTIVE Computed tomography (CT) provides rich diagnosis and severity information of COVID-19 in clinical practice. However, there is no computerized tool to automatically delineate COVID-19 infection regions in chest CT scans for quantitative assessment in advanced applications such as severity prediction. The aim of this study was to develop a deep learning (DL)-based method for automatic segmentation and quantification of infection regions as well as the entire lungs from chest CT scans. METHODS The DL-based segmentation method employs the "VB-Net" neural network to segment COVID-19 infection regions in CT scans. The developed DL-based segmentation system is trained by CT scans from 249 COVID-19 patients, and further validated by CT scans from other 300 COVID-19 patients. To accelerate the manual delineation of CT scans for training, a human-involved-model-iterations (HIMI) strategy is also adopted to assist radiologists to refine automatic annotation of each training case. To evaluate the performance of the DL-based segmentation system, three metrics, that is, Dice similarity coefficient, the differences of volume, and percentage of infection (POI), are calculated between automatic and manual segmentations on the validation set. Then, a clinical study on severity prediction is reported based on the quantitative infection assessment. RESULTS The proposed DL-based segmentation system yielded Dice similarity coefficients of 91.6% ± 10.0% between automatic and manual segmentations, and a mean POI estimation error of 0.3% for the whole lung on the validation dataset. Moreover, compared with the cases with fully manual delineation that often takes hours, the proposed HIMI training strategy can dramatically reduce the delineation time to 4 min after three iterations of model updating. Besides, the best accuracy of severity prediction was 73.4% ± 1.3% when the mass of infection (MOI) of multiple lung lobes and bronchopulmonary segments were used as features for severity prediction, indicating the potential clinical application of our quantification technique on severity prediction. CONCLUSIONS A DL-based segmentation system has been developed to automatically segment and quantify infection regions in CT scans of COVID-19 patients. Quantitative evaluation indicated high accuracy in automatic infection delineation and severity prediction.
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Affiliation(s)
- Fei Shan
- Department of RadiologyShanghai Public Health Clinical CenterFudan UniversityShanghai201508China
| | - Yaozong Gao
- Department of Research and DevelopmentShanghai United Imaging Intelligence Co., Ltd.Shanghai200232China
| | - Jun Wang
- Key Laboratory of Specialty Fiber Optics and Optical Access NetworksJoint International Research Laboratory of Specialty Fiber Optics and Advanced CommunicationShanghai Institute for Advanced Communication and Data ScienceSchool of Communication & Information EngineeringShanghai UniversityShanghai200444China
| | - Weiya Shi
- Department of RadiologyShanghai Public Health Clinical CenterFudan UniversityShanghai201508China
| | - Nannan Shi
- Department of RadiologyShanghai Public Health Clinical CenterFudan UniversityShanghai201508China
| | - Miaofei Han
- Department of Research and DevelopmentShanghai United Imaging Intelligence Co., Ltd.Shanghai200232China
| | - Zhong Xue
- Department of Research and DevelopmentShanghai United Imaging Intelligence Co., Ltd.Shanghai200232China
| | - Dinggang Shen
- Department of Research and DevelopmentShanghai United Imaging Intelligence Co., Ltd.Shanghai200232China
- School of Biomedical EngineeringShanghaiTech UniversityShanghaiChina
- Department of Artificial IntelligenceKorea UniversitySeoul02841Republic of Korea
| | - Yuxin Shi
- Department of RadiologyShanghai Public Health Clinical CenterFudan UniversityShanghai201508China
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Sun L, Wang Z, Pu H, Yuan G, Guo L, Pu T, Peng Z. Attention-embedded complementary-stream CNN for false positive reduction in pulmonary nodule detection. Comput Biol Med 2021; 133:104357. [PMID: 33836449 DOI: 10.1016/j.compbiomed.2021.104357] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 03/22/2021] [Accepted: 03/22/2021] [Indexed: 01/18/2023]
Abstract
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.
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Affiliation(s)
- Lingma Sun
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China; Laboratory of Imaging Detection and Intelligent Perception, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Zhuoran Wang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China; Laboratory of Imaging Detection and Intelligent Perception, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Hong Pu
- Sichuan Provincial People's Hospital, Chengdu, Sichuan, 610072, China; School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China
| | - Guohui Yuan
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China; Laboratory of Imaging Detection and Intelligent Perception, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Lu Guo
- Sichuan Provincial People's Hospital, Chengdu, Sichuan, 610072, China; School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China
| | - Tian Pu
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China; Laboratory of Imaging Detection and Intelligent Perception, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Zhenming Peng
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China; Laboratory of Imaging Detection and Intelligent Perception, University of Electronic Science and Technology of China, Chengdu, 611731, China.
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Radiologic Assessment of Osteosarcoma Lung Metastases: State of the Art and Recent Advances. Cells 2021; 10:cells10030553. [PMID: 33806513 PMCID: PMC7999261 DOI: 10.3390/cells10030553] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 02/28/2021] [Accepted: 03/02/2021] [Indexed: 12/14/2022] Open
Abstract
The lung is the most frequent site of osteosarcoma (OS) metastases, which are a critical point in defining a patient’s prognosis. Chest computed tomography (CT) represents the gold standard for the detection of lung metastases even if its sensitivity widely ranges in the literature since lung localizations are often atypical. ESMO guidelines represent one of the major references for the follow-up program of OS patients. The development of new reconstruction techniques, such as the iterative method and the deep learning-based image reconstruction (DLIR), has led to a significant reduction of the radiation dose with the low-dose CT. The improvement of these techniques has great importance considering the young-onset of the disease and the strict chest surveillance during follow-up programs. The use of 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT is still controversial, while volume doubling time (VDT) and computer-aided diagnosis (CAD) systems are recent diagnostic tools that could support radiologists for lung nodules evaluation. Their use, well-established for other malignancies, needs to be further evaluated, focusing on OS patients.
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Su Y, Li D, Chen X. Lung Nodule Detection based on Faster R-CNN Framework. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 200:105866. [PMID: 33309304 DOI: 10.1016/j.cmpb.2020.105866] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 11/17/2020] [Indexed: 06/12/2023]
Abstract
BACKGROUND Lung cancer is a worldwide high-risk disease, and lung nodules are the main manifestation of early lung cancer. Automatic detection of lung nodules reduces the workload of radiologists, the rate of misdiagnosis and missed diagnosis. For this purpose, we propose a Faster R-CNN algorithm for the detection of these lung nodules. METHOD Faster R-CNN algorithm can detect lung nodules, and the training set is used to prove the feasibility of this technique. In theory, parameter optimization can improve network structure, as well as detection accuracy. RESULT Through experiments, the best parameters are that the basic learning rate is 0.001, step size is 70,000, attenuation coefficient is 0.1, the value of Dropout is 0.5, and the value of Batch Size is 64. Compared with other networks for detecting lung nodules, the optimized and improved algorithm proposed in this paper generally improves detection accuracy by more than 20% when compared with the other traditional algorithms. CONCLUSION Our experimental results have proved that the method of detecting lung nodules based on Faster R-CNN algorithm has good accuracy and therefore, presents potential clinical value in lung disease diagnosis. This method can further assist radiologists, and also for researchers in the design and development of the detection system for lung nodules.
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Affiliation(s)
- Ying Su
- Department of Nursing, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110000, China
| | - Dan Li
- Department of Nursing, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110000, China
| | - Xiaodong Chen
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110000, China.
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Park S, Lee SM, Kim W, Park H, Jung KH, Do KH, Seo JB. Computer-aided Detection of Subsolid Nodules at Chest CT: Improved Performance with Deep Learning-based CT Section Thickness Reduction. Radiology 2021; 299:211-219. [PMID: 33560190 DOI: 10.1148/radiol.2021203387] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Studies on the optimal CT section thickness for detecting subsolid nodules (SSNs) with computer-aided detection (CAD) are lacking. Purpose To assess the effect of CT section thickness on CAD performance in the detection of SSNs and to investigate whether deep learning-based super-resolution algorithms for reducing CT section thickness can improve performance. Materials and Methods CT images obtained with 1-, 3-, and 5-mm-thick sections were obtained in patients who underwent surgery between March 2018 and December 2018. Patients with resected synchronous SSNs and those without SSNs (negative controls) were retrospectively evaluated. The SSNs, which ranged from 6 to 30 mm, were labeled ground-truth lesions. A deep learning-based CAD system was applied to SSN detection on CT images of each section thickness and those converted from 3- and 5-mm section thickness into 1-mm section thickness by using the super-resolution algorithm. The CAD performance on each section thickness was evaluated and compared by using the jackknife alternative free response receiver operating characteristic figure of merit. Results A total of 308 patients (mean age ± standard deviation, 62 years ± 10; 183 women) with 424 SSNs (310 part-solid and 114 nonsolid nodules) and 182 patients without SSNs (mean age, 65 years ± 10; 97 men) were evaluated. The figures of merit differed across the three section thicknesses (0.92, 0.90, and 0.89 for 1, 3, and 5 mm, respectively; P = .04) and between 1- and 5-mm sections (P = .04). The figures of merit varied for nonsolid nodules (0.78, 0.72, and 0.66 for 1, 3, and 5 mm, respectively; P < .001) but not for part-solid nodules (range, 0.93-0.94; P = .76). The super-resolution algorithm improved CAD sensitivity on 3- and 5-mm-thick sections (P = .02 for 3 mm, P < .001 for 5 mm). Conclusion Computer-aided detection (CAD) of subsolid nodules performed better at 1-mm section thickness CT than at 3- and 5-mm section thickness CT, particularly with nonsolid nodules. Application of a super-resolution algorithm improved the sensitivity of CAD at 3- and 5-mm section thickness CT. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Goo in this issue.
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Affiliation(s)
- Sohee Park
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul 138-736, Korea (S.P., S.M.L., W.K., K.H.D., J.B.S.); and VUNO, Seoul, South Korea (H.P., K.H.J.)
| | - Sang Min Lee
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul 138-736, Korea (S.P., S.M.L., W.K., K.H.D., J.B.S.); and VUNO, Seoul, South Korea (H.P., K.H.J.)
| | - Wooil Kim
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul 138-736, Korea (S.P., S.M.L., W.K., K.H.D., J.B.S.); and VUNO, Seoul, South Korea (H.P., K.H.J.)
| | - Hyunho Park
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul 138-736, Korea (S.P., S.M.L., W.K., K.H.D., J.B.S.); and VUNO, Seoul, South Korea (H.P., K.H.J.)
| | - Kyu-Hwan Jung
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul 138-736, Korea (S.P., S.M.L., W.K., K.H.D., J.B.S.); and VUNO, Seoul, South Korea (H.P., K.H.J.)
| | - Kyung-Hyun Do
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul 138-736, Korea (S.P., S.M.L., W.K., K.H.D., J.B.S.); and VUNO, Seoul, South Korea (H.P., K.H.J.)
| | - Joon Beom Seo
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul 138-736, Korea (S.P., S.M.L., W.K., K.H.D., J.B.S.); and VUNO, Seoul, South Korea (H.P., K.H.J.)
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Shi W, Peng X, Liu T, Cheng Z, Lu H, Yang S, Zhang J, Wang M, Gao Y, Shi Y, Zhang Z, Shan F. A deep learning-based quantitative computed tomography model for predicting the severity of COVID-19: a retrospective study of 196 patients. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:216. [PMID: 33708843 PMCID: PMC7940921 DOI: 10.21037/atm-20-2464] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Background The assessment of the severity of coronavirus disease 2019 (COVID-19) by clinical presentation has not met the urgent clinical need so far. We aimed to establish a deep learning (DL) model based on quantitative computed tomography (CT) and initial clinical features to predict the severity of COVID-19. Methods One hundred ninety-six hospitalized patients with confirmed COVID-19 were enrolled from January 20 to February 10, 2020 in our centre, and were divided into severe and non-severe groups. The clinico-radiological data on admission were retrospectively collected and compared between the two groups. The optimal clinico-radiological features were determined based on least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and a predictive nomogram model was established by five-fold cross-validation. Receiver operating characteristic (ROC) analyses were conducted, and the areas under the receiver operating characteristic curve (AUCs) of the nomogram model, quantitative CT parameters that were significant in univariate analysis, and pneumonia severity index (PSI) were compared. Results In comparison with the non-severe group (151 patients), the severe group (45 patients) had a higher PSI (P<0.001). DL-based quantitative CT indicated that the mass of infection (MOICT) and the percentage of infection (POICT) in the whole lung were higher in the severe group (both P<0.001). The nomogram model was based on MOICT and clinical features, including age, cluster of differentiation 4 (CD4)+ T cell count, serum lactate dehydrogenase (LDH), and C-reactive protein (CRP). The AUC values of the model, MOICT, POICT, and PSI scores were 0.900, 0.813, 0.805, and 0.751, respectively. The nomogram model performed significantly better than the other three parameters in predicting severity (P=0.003, P=0.001, and P<0.001, respectively). Conclusions Although quantitative CT parameters and the PSI can well predict the severity of COVID-19, the DL-based quantitative CT model is more efficient.
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Affiliation(s)
- Weiya Shi
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Xueqing Peng
- Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Tiefu Liu
- Department of Scientific Research, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Zenghui Cheng
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongzhou Lu
- Department of Infectious Disease, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Shuyi Yang
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Jiulong Zhang
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Mei Wang
- Department of Respiration, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Yaozong Gao
- Department of Research and Development, Shanghai United Imaging Intelligence Inc., Shanghai, China
| | - Yuxin Shi
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Zhiyong Zhang
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Fei Shan
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
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Shi W, Peng X, Liu T, Cheng Z, Lu H, Yang S, Zhang J, Wang M, Gao Y, Shi Y, Zhang Z, Shan F. A deep learning-based quantitative computed tomography model for predicting the severity of COVID-19: a retrospective study of 196 patients. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:216. [PMID: 33708843 DOI: 10.2139/ssrn.3546089] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
BACKGROUND The assessment of the severity of coronavirus disease 2019 (COVID-19) by clinical presentation has not met the urgent clinical need so far. We aimed to establish a deep learning (DL) model based on quantitative computed tomography (CT) and initial clinical features to predict the severity of COVID-19. METHODS One hundred ninety-six hospitalized patients with confirmed COVID-19 were enrolled from January 20 to February 10, 2020 in our centre, and were divided into severe and non-severe groups. The clinico-radiological data on admission were retrospectively collected and compared between the two groups. The optimal clinico-radiological features were determined based on least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and a predictive nomogram model was established by five-fold cross-validation. Receiver operating characteristic (ROC) analyses were conducted, and the areas under the receiver operating characteristic curve (AUCs) of the nomogram model, quantitative CT parameters that were significant in univariate analysis, and pneumonia severity index (PSI) were compared. RESULTS In comparison with the non-severe group (151 patients), the severe group (45 patients) had a higher PSI (P<0.001). DL-based quantitative CT indicated that the mass of infection (MOICT) and the percentage of infection (POICT) in the whole lung were higher in the severe group (both P<0.001). The nomogram model was based on MOICT and clinical features, including age, cluster of differentiation 4 (CD4)+ T cell count, serum lactate dehydrogenase (LDH), and C-reactive protein (CRP). The AUC values of the model, MOICT, POICT, and PSI scores were 0.900, 0.813, 0.805, and 0.751, respectively. The nomogram model performed significantly better than the other three parameters in predicting severity (P=0.003, P=0.001, and P<0.001, respectively). CONCLUSIONS Although quantitative CT parameters and the PSI can well predict the severity of COVID-19, the DL-based quantitative CT model is more efficient.
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Affiliation(s)
- Weiya Shi
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Xueqing Peng
- Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Tiefu Liu
- Department of Scientific Research, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Zenghui Cheng
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongzhou Lu
- Department of Infectious Disease, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Shuyi Yang
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Jiulong Zhang
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Mei Wang
- Department of Respiration, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Yaozong Gao
- Department of Research and Development, Shanghai United Imaging Intelligence Inc., Shanghai, China
| | - Yuxin Shi
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Zhiyong Zhang
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Fei Shan
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
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Multiscale CNN with compound fusions for false positive reduction in lung nodule detection. Artif Intell Med 2021; 113:102017. [PMID: 33685584 DOI: 10.1016/j.artmed.2021.102017] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2019] [Revised: 07/18/2020] [Accepted: 07/21/2020] [Indexed: 12/20/2022]
Abstract
Pulmonary lung nodules are often benign at the early stage but they could easily become malignant and metastasize to other locations in later stages. Morphological characteristics of these nodule instances vary largely in terms of their size, shape, and texture. There are also other co-existing lung anatomical structures such as lung walls and blood vessels surrounding these nodules resulting in complex contextual information. As a result, their early diagnosis to enable decisive intervention using Computer-Aided Diagnosis (CAD) systems face serious challenges, especially at low false positive rates. In this paper, we propose a new Convolutional Neural Network (CNN) architecture called Multiscale CNN with Compound Fusions (MCNN-CF) for this purpose which uses multiscale 3D patches as inputs and performs a fusion of intermediate features at two different depths of the network in two diverse fashions. The network is trained by a new iterative training procedure adapted to circumvent the class imbalance problem and obtained a Competitive Performance Metric (CPM) score of 0.948 when tested on the LUNA16 dataset. Experimental results illustrate the robustness of the proposed system which has increased the confidence of the prediction probabilities in the detection of the most variety of nodules.
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Wu Z, Ge R, Shi G, Zhang L, Chen Y, Luo L, Cao Y, Yu H. MD-NDNet: a multi-dimensional convolutional neural network for false-positive reduction in pulmonary nodule detection. Phys Med Biol 2020; 65:235053. [PMID: 32698172 DOI: 10.1088/1361-6560/aba87c] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Pulmonary nodule false-positive reduction is of great significance for automated nodule detection in clinical diagnosis of low-dose computed tomography (LDCT) lung cancer screening. Due to individual intra-nodule variations and visual similarities between true nodules and false positives as soft tissues in LDCT images, the current clinical practices remain subject to shortcomings of potential high-risk and time-consumption issues. In this paper, we propose a multi-dimensional nodule detection network (MD-NDNet) for automatic nodule false-positive reduction using deep convolutional neural network (DCNNs). The underlying method collaboratively integrates multi-dimensional nodule information to complementarily and comprehensively extract nodule inter-plane volumetric correlation features using three-dimensional CNNs (3D CNNs) and spatial nodule correlation features from sagittal, coronal, and axial planes using two-dimensional CNNs (2D CNNs) with attention module. To incorporate different sizes and shapes of nodule candidates, a multi-scale ensemble strategy is employed for probability aggregation with weights. The proposed method is evaluated on the LUNA16 challenge dataset in ISBI 2016 with ten-fold cross-validation. Experiment results show that the proposed framework achieves classification performance with a CPM score of 0.9008. All of these indicate that our method enables an efficient, accurate and reliable pulmonary nodule detection for clinical diagnosis.
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Affiliation(s)
- Zhan Wu
- School of Cyberspace Security, Southeast University, Nanjing, Jiangsu, China. Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, United States of America
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Lu X, Gu Y, Yang L, Zhang B, Zhao Y, Yu D, Zhao J, Gao L, Zhou T, Liu Y, Zhang W. Multi-level 3D Densenets for False-positive Reduction in Lung Nodule Detection Based on Chest Computed Tomography. Curr Med Imaging 2020; 16:1004-1021. [PMID: 33081662 DOI: 10.2174/1573405615666191113122840] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2019] [Revised: 10/11/2019] [Accepted: 10/19/2019] [Indexed: 12/31/2022]
Abstract
OBJECTIVE False-positive nodule reduction is a crucial part of a computer-aided detection (CADe) system, which assists radiologists in accurate lung nodule detection. In this research, a novel scheme using multi-level 3D DenseNet framework is proposed to implement false-positive nodule reduction task. METHODS Multi-level 3D DenseNet models were extended to differentiate lung nodules from falsepositive nodules. First, different models were fed with 3D cubes with different sizes for encoding multi-level contextual information to meet the challenges of the large variations of lung nodules. In addition, image rotation and flipping were utilized to upsample positive samples which consisted of a positive sample set. Furthermore, the 3D DenseNets were designed to keep low-level information of nodules, as densely connected structures in DenseNet can reuse features of lung nodules and then boost feature propagation. Finally, the optimal weighted linear combination of all model scores obtained the best classification result in this research. RESULTS The proposed method was evaluated with LUNA16 dataset which contained 888 thin-slice CT scans. The performance was validated via 10-fold cross-validation. Both the Free-response Receiver Operating Characteristic (FROC) curve and the Competition Performance Metric (CPM) score show that the proposed scheme can achieve a satisfactory detection performance in the falsepositive reduction track of the LUNA16 challenge. CONCLUSION The result shows that the proposed scheme can be significant for false-positive nodule reduction task.
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Affiliation(s)
- Xiaoqi Lu
- College of Information Engineering, Inner Mongolia University of Technology, Hohhot, 010051, China,Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China,School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China
| | - Yu Gu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China,School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China
| | - Lidong Yang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China
| | - Baohua Zhang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China
| | - Ying Zhao
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China
| | - Dahua Yu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China
| | - Jianfeng Zhao
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China
| | - Lixin Gao
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China,School of Foreign Languages, Inner Mongolia University of Science and Technology, Baotou, 014010, China
| | - Tao Zhou
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China
| | - Yang Liu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China
| | - Wei Zhang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China
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