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Siddiqui EA, Chaurasia V, Shandilya M. Classification of lung cancer computed tomography images using a 3-dimensional deep convolutional neural network with multi-layer filter. J Cancer Res Clin Oncol 2023; 149:11279-11294. [PMID: 37368121 DOI: 10.1007/s00432-023-04992-9] [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: 05/03/2023] [Accepted: 06/15/2023] [Indexed: 06/28/2023]
Abstract
Lung cancer creates pulmonary nodules in the patient's lung, which may be diagnosed early on using computer-aided diagnostics. A novel automated pulmonary nodule diagnosis technique using three-dimensional deep convolutional neural networks and multi-layered filter has been presented in this paper. For the suggested automated diagnosis of lung nodule, volumetric computed tomographic images are employed. The proposed approach generates three-dimensional feature layers, which retain the temporal links between adjacent slices of computed tomographic images. The use of several activation functions at different levels of the proposed network results in increased feature extraction and efficient classification. The suggested approach divides lung volumetric computed tomography pictures into malignant and benign categories. The suggested technique's performance is evaluated using three commonly used datasets in the domain: LUNA 16, LIDC-IDRI, and TCIA. The proposed method outperforms the state-of-the-art in terms of accuracy, sensitivity, specificity, F-1 score, false-positive rate, false-negative rate, and error rate.
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Affiliation(s)
| | | | - Madhu Shandilya
- Maulana Azad National Institute of Technology, Bhopal, 462003, India
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2
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Tsivgoulis M, Papastergiou T, Megalooikonomou V. An improved SqueezeNet model for the diagnosis of lung cancer in CT scans. MACHINE LEARNING WITH APPLICATIONS 2022. [DOI: 10.1016/j.mlwa.2022.100399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022] Open
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3
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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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4
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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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5
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Khan MA, Rajinikanth V, Satapathy SC, Taniar D, Mohanty JR, Tariq U, Damaševičius R. VGG19 Network Assisted Joint Segmentation and Classification of Lung Nodules in CT Images. Diagnostics (Basel) 2021; 11:2208. [PMID: 34943443 PMCID: PMC8699868 DOI: 10.3390/diagnostics11122208] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 11/17/2021] [Accepted: 11/24/2021] [Indexed: 12/27/2022] Open
Abstract
Pulmonary nodule is one of the lung diseases and its early diagnosis and treatment are essential to cure the patient. This paper introduces a deep learning framework to support the automated detection of lung nodules in computed tomography (CT) images. The proposed framework employs VGG-SegNet supported nodule mining and pre-trained DL-based classification to support automated lung nodule detection. The classification of lung CT images is implemented using the attained deep features, and then these features are serially concatenated with the handcrafted features, such as the Grey Level Co-Occurrence Matrix (GLCM), Local-Binary-Pattern (LBP) and Pyramid Histogram of Oriented Gradients (PHOG) to enhance the disease detection accuracy. The images used for experiments are collected from the LIDC-IDRI and Lung-PET-CT-Dx datasets. The experimental results attained show that the VGG19 architecture with concatenated deep and handcrafted features can achieve an accuracy of 97.83% with the SVM-RBF classifier.
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Affiliation(s)
| | - Venkatesan Rajinikanth
- Department of Electronics and Instrumentation Engineering, St. Joseph’s College of Engineering, Chennai, Tamilnadu 600119, India;
| | - Suresh Chandra Satapathy
- School of Computer Engineering, Kalinga Institute of Industrial Technology (Deemed to Be University), Bhubaneswar, Odisha 751024, India;
| | - David Taniar
- Faculty of Information Technology, Monash University, Clayton, VIC 3800, Australia;
| | - Jnyana Ranjan Mohanty
- School of Computer Applications, Kalinga Institute of Industrial Technology (Deemed to Be University), Bhubaneswar, Odisha 751024, India;
| | - Usman Tariq
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia;
| | - Robertas Damaševičius
- Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
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6
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CAD system for lung nodule detection using deep learning with CNN. Med Biol Eng Comput 2021; 60:221-228. [PMID: 34811644 DOI: 10.1007/s11517-021-02462-3] [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: 11/06/2020] [Accepted: 09/29/2021] [Indexed: 10/19/2022]
Abstract
The early detection of pulmonary nodules using computer-aided diagnosis (CAD) systems is very essential in reducing mortality rates of lung cancer. In this paper, we propose a new deep learning approach to improve the classification accuracy of pulmonary nodules in computed tomography (CT) images. Our proposed CNN-5CL (convolutional neural network with 5 convolutional layers) approach uses an 11-layer convolutional neural network (with 5 convolutional layers) for automatic feature extraction and classification. The proposed method is evaluated using LIDC/IDRI images. The proposed method is implemented in the Python platform, and the performance is evaluated with metrics such as accuracy, sensitivity, specificity, and receiver operating characteristics (ROC). The results show that the proposed method achieves accuracy, sensitivity, specificity, and area under the roc curve (AUC) of 98.88%, 99.62%, 93.73%, and 0.928, respectively. The proposed approach outperforms various other methods such as Naïve Bayes, K-nearest neighbor, support vector machine, adaptive neuro fuzzy inference system methods, and also other deep learning-based approaches.
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7
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Ziyad SR, Radha V, Vayyapuri T. Overview of Computer Aided Detection and Computer Aided Diagnosis Systems for Lung Nodule Detection in Computed Tomography. Curr Med Imaging 2020; 16:16-26. [PMID: 31989890 DOI: 10.2174/1573405615666190206153321] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 01/02/2019] [Accepted: 01/10/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND Lung cancer has become a major cause of cancer-related deaths. Detection of potentially malignant lung nodules is essential for the early diagnosis and clinical management of lung cancer. In clinical practice, the interpretation of Computed Tomography (CT) images is challenging for radiologists due to a large number of cases. There is a high rate of false positives in the manual findings. Computer aided detection system (CAD) and computer aided diagnosis systems (CADx) enhance the radiologists in accurately delineating the lung nodules. OBJECTIVES The objective is to analyze CAD and CADx systems for lung nodule detection. It is necessary to review the various techniques followed in CAD and CADx systems proposed and implemented by various research persons. This study aims at analyzing the recent application of various concepts in computer science to each stage of CAD and CADx. METHODS This review paper is special in its own kind because it analyses the various techniques proposed by different eminent researchers in noise removal, contrast enhancement, thorax removal, lung segmentation, bone suppression, segmentation of trachea, classification of nodule and nonnodule and final classification of benign and malignant nodules. RESULTS A comparison of the performance of different techniques implemented by various researchers for the classification of nodule and non-nodule has been tabulated in the paper. CONCLUSION The findings of this review paper will definitely prove to be useful to the research community working on automation of lung nodule detection.
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Affiliation(s)
- Shabana Rasheed Ziyad
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin AbdulAziz University, Al Kharj, Saudi Arabia
| | - Venkatachalam Radha
- Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India
| | - Thavavel Vayyapuri
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin AbdulAziz University, Al Kharj, Saudi Arabia
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8
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Bonavita I, Rafael-Palou X, Ceresa M, Piella G, Ribas V, González Ballester MA. Integration of convolutional neural networks for pulmonary nodule malignancy assessment in a lung cancer classification pipeline. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 185:105172. [PMID: 31710985 DOI: 10.1016/j.cmpb.2019.105172] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 08/13/2019] [Accepted: 10/31/2019] [Indexed: 05/23/2023]
Abstract
The early identification of malignant pulmonary nodules is critical for a better lung cancer prognosis and less invasive chemo or radio therapies. Nodule malignancy assessment done by radiologists is extremely useful for planning a preventive intervention but is, unfortunately, a complex, time-consuming and error-prone task. This explains the lack of large datasets containing radiologists malignancy characterization of nodules; METHODS: In this article, we propose to assess nodule malignancy through 3D convolutional neural networks and to integrate it in an automated end-to-end existing pipeline of lung cancer detection. For training and testing purposes we used independent subsets of the LIDC dataset; RESULTS: Adding the probabilities of nodules malignity in a baseline lung cancer pipeline improved its F1-weighted score by 14.7%, whereas integrating the malignancy model itself using transfer learning outperformed the baseline prediction by 11.8% of F1-weighted score; CONCLUSIONS: Despite the limited size of the lung cancer datasets, integrating predictive models of nodule malignancy improves prediction of lung cancer.
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Affiliation(s)
- Ilaria Bonavita
- Eurecat, Centre Tecnològic de Catalunya, eHealth Unit, Barcelona, Spain.
| | - Xavier Rafael-Palou
- Eurecat, Centre Tecnològic de Catalunya, eHealth Unit, Barcelona, Spain; BCN Medtech, Dept. of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Mario Ceresa
- BCN Medtech, Dept. of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Gemma Piella
- BCN Medtech, Dept. of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Vicent Ribas
- Eurecat, Centre Tecnològic de Catalunya, eHealth Unit, Barcelona, Spain.
| | - Miguel A González Ballester
- BCN Medtech, Dept. of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; ICREA, Barcelona, Spain.
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9
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Spectral analysis for pulmonary nodule detection using the optimal fractional S-Transform. Comput Biol Med 2020; 119:103675. [PMID: 32339120 DOI: 10.1016/j.compbiomed.2020.103675] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 02/23/2020] [Accepted: 02/23/2020] [Indexed: 01/18/2023]
Abstract
Different frequency components of the lung, which have not been fully considered in traditional computer-aided detection systems for pulmonary nodules, can cause heterogeneous energy distribution. Hence, spectral analysis, which is an important time-frequency representation tool, is utilized to characterize the frequency-dependent energy responses of nodules. In this study, a novel spectral-analysis-based method for nodule candidate detection is presented. The optimal fractional S-transform is applied to transform raw computed tomography images from the spatial to time-frequency domain. Next, a time-frequency cube is decomposed using spectral decomposition to a frequency-dependent energy slice. Subsequently, an energy distribution is obtained by the Teager-Kaiser energy (TKE) to characterize the nodules. Finally, nodule candidates are detected using rule-based and threshold algorithms in the TKE image. The proposed method is validated on a clinical CT data set from Sichuan Provincial People's Hospital. The signal-to-clutter ratio (SCR) increases by 35.5% with respect to raw CT slices. Furthermore, the proposed method exhibits a sensitivity of 97.87%, with only 6.8 false positives per slice. The total number of nodule candidates has an average reduction of 50%. The results indicate that the time-frequency features can effectively characterize solid nodules. Moreover, the proposed method demonstrates accurate detection and can reduce the number of false positive efficiently.
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10
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A Computer-Aided Detection System for the Detection of Lung Nodules Based on 3D-ResNet. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9245544] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
In recent years, the research into automatic aided detection systems for pulmonary nodules has been extremely active. Most of the existing studies are based on 2D convolution neural networks, which cannot make full use of computed tomography’s (CT) 3D spatial information. To address this problem, a computer-aided detection (CAD) system for lung nodules based on a 3D residual network (3D-ResNet) inspired by cognitive science is proposed in this paper. In this system, we feed the slice information extracted from three different axis planes into the U-NET network set, and make the joint decision to generate a candidate nodule set, which is the input of the proposed 3D residual network after extraction. We extracted 3D samples with 40, 44, 48, 52, and 56 mm sides from each candidate nodule in the candidate set and feed them into the trained residual network to get the probability of positive nodule after re-sampling the 3D sample to
48
×
48
×
48
mm
3
. Finally, a joint judgment is made based on the probabilities of five 3D samples of different sizes to obtain the final result. Random rotation and translation and data amplification technology are used to prevent overfitting during network training. The detection intensity on the largest public data set (i.e., the Lung Image Database Consortium and Image Database Resource Initiative—LIDC-IDRI) reached 86.5% and 92.3% at 1 frame per second (FPs) and 4 FPs respectively using our algorithm, which is better than most CAD systems using 2D convolutional neural networks. In addition, a 3D residual network and a multi-section 2D convolution neural network were tested on the unrelated Tianchi dataset. The results indicate that 3D-ResNet has better feature extraction ability than multi-section 2D-ConvNet and is more suitable for reduction of false positive nodules.
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11
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Gruetzemacher R, Gupta A, Paradice D. 3D deep learning for detecting pulmonary nodules in CT scans. J Am Med Inform Assoc 2019; 25:1301-1310. [PMID: 30137371 DOI: 10.1093/jamia/ocy098] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Accepted: 07/03/2018] [Indexed: 01/09/2023] Open
Abstract
Objective To demonstrate and test the validity of a novel deep-learning-based system for the automated detection of pulmonary nodules. Materials and Methods The proposed system uses 2 3D deep learning models, 1 for each of the essential tasks of computer-aided nodule detection: candidate generation and false positive reduction. A total of 888 scans from the LIDC-IDRI dataset were used for training and evaluation. Results Results for candidate generation on the test data indicated a detection rate of 94.77% with 30.39 false positives per scan, while the test results for false positive reduction exhibited a sensitivity of 94.21% with 1.789 false positives per scan. The overall system detection rate on the test data was 89.29% with 1.789 false positives per scan. Discussion An extensive and rigorous validation was conducted to assess the performance of the proposed system. The system demonstrated a novel combination of 3D deep neural network architectures and demonstrates the use of deep learning for both candidate generation and false positive reduction to be evaluated with a substantial test dataset. The results strongly support the ability of deep learning pulmonary nodule detection systems to generalize to unseen data. The source code and trained model weights have been made available. Conclusion A novel deep-neural-network-based pulmonary nodule detection system is demonstrated and validated. The results provide comparison of the proposed deep-learning-based system over other similar systems based on performance.
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Affiliation(s)
- Ross Gruetzemacher
- Department of Systems & Technology, Raymond J. Harbert College of Business, Auburn University, Auburn, AL, USA 36849
| | - Ashish Gupta
- Department of Systems & Technology, Raymond J. Harbert College of Business, Auburn University, Auburn, AL, USA 36849
| | - David Paradice
- Department of Systems & Technology, Raymond J. Harbert College of Business, Auburn University, Auburn, AL, USA 36849
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12
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Masood A, Yang P, Sheng B, Li H, Li P, Qin J, Lanfranchi V, Kim J, Feng DD. Cloud-Based Automated Clinical Decision Support System for Detection and Diagnosis of Lung Cancer in Chest CT. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2019; 8:4300113. [PMID: 31929952 PMCID: PMC6946021 DOI: 10.1109/jtehm.2019.2955458] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2019] [Revised: 09/02/2019] [Accepted: 11/08/2019] [Indexed: 12/29/2022]
Abstract
Lung cancer is a major cause for cancer-related deaths. The detection of pulmonary cancer in the early stages can highly increase survival rate. Manual delineation of lung nodules by radiologists is a tedious task. We developed a novel computer-aided decision support system for lung nodule detection based on a 3D Deep Convolutional Neural Network (3DDCNN) for assisting the radiologists. Our decision support system provides a second opinion to the radiologists in lung cancer diagnostic decision making. In order to leverage 3-dimensional information from Computed Tomography (CT) scans, we applied median intensity projection and multi-Region Proposal Network (mRPN) for automatic selection of potential region-of-interests. Our Computer Aided Diagnosis (CAD) system has been trained and validated using LUNA16, ANODE09, and LIDC-IDR datasets; the experiments demonstrate the superior performance of our system, attaining sensitivity, specificity, AUROC, accuracy, of 98.4%, 92%, 96% and 98.51% with 2.1 FPs per scan. We integrated cloud computing, trained and validated our Cloud-Based 3DDCNN on the datasets provided by Shanghai Sixth People's Hospital, as well as LUNA16, ANODE09, and LIDC-IDR. Our system outperformed the state-of-the-art systems and obtained an impressive 98.7% sensitivity at 1.97 FPs per scan. This shows the potentials of deep learning, in combination with cloud computing, for accurate and efficient lung nodule detection via CT imaging, which could help doctors and radiologists in treating lung cancer patients.
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Affiliation(s)
- Anum Masood
- Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghai200240China
| | - Po Yang
- Department of Computer ScienceUniversity of SheffieldSheffieldS1 4DPU.K.
| | - Bin Sheng
- Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghai200240China
| | - Huating Li
- Shanghai Jiao Tong University Affiliated Sixth People’s HospitalShanghai200233China
| | - Ping Li
- Department of ComputingThe Hong Kong Polytechnic UniversityHong Kong
| | - Jing Qin
- Centre for Smart Health, School of NursingThe Hong Kong Polytechnic UniversityHong Kong
| | | | - Jinman Kim
- Biomedical and Multimedia Information Technology Research Group, School of Information TechnologiesThe University of SydneySydneyNSW2006Australia
| | - David Dagan Feng
- Biomedical and Multimedia Information Technology Research Group, School of Information TechnologiesThe University of SydneySydneyNSW2006Australia
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13
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Shaukat F, Raja G, Frangi AF. Computer-aided detection of lung nodules: a review. J Med Imaging (Bellingham) 2019. [DOI: 10.1117/1.jmi.6.2.020901] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Furqan Shaukat
- University of Engineering and Technology, Department of Electrical Engineering, Taxila
| | - Gulistan Raja
- University of Engineering and Technology, Department of Electrical Engineering, Taxila
| | - Alejandro F. Frangi
- University of Leeds Woodhouse Lane, School of Computing and School of Medicine, Leeds
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14
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Khan SA, Nazir M, Khan MA, Saba T, Javed K, Rehman A, Akram T, Awais M. Lungs nodule detection framework from computed tomography images using support vector machine. Microsc Res Tech 2019; 82:1256-1266. [PMID: 30974031 DOI: 10.1002/jemt.23275] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 02/21/2019] [Accepted: 03/31/2019] [Indexed: 11/11/2022]
Affiliation(s)
- Sajid A. Khan
- Department of Computer ScienceShaheed Zulfikar Ali Bhutto Institute of Science and Technology Islamabad Pakistan
- Department of Software EngineeringFoundation University Islamabad Pakistan
| | - Muhammad Nazir
- Department of CS & EHITEC University Taxila Cantonment Pakistan
| | | | - Tanzila Saba
- College of Computer and Information SciencesPrince Sultan University Riyadh Saudi Arabia
| | - Kashif Javed
- Department of RoboticsSMME NUST Islamabad Pakistan
| | - Amjad Rehman
- College of Business AdministrationAl Yamamah University Riyadh Saudi Arabia
| | - Tallha Akram
- Department of EECOMSATS University Islamabad, Wah Campus Islamabad Pakistan
| | - Muhammad Awais
- Department of EECOMSATS University Islamabad, Wah Campus Islamabad Pakistan
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15
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Khan SA, Hussain S, Yang S, Iqbal K. Effective and Reliable Framework for Lung Nodules Detection from CT Scan Images. Sci Rep 2019; 9:4989. [PMID: 30899052 PMCID: PMC6428823 DOI: 10.1038/s41598-019-41510-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Accepted: 03/11/2019] [Indexed: 12/01/2022] Open
Abstract
Lung cancer is considered more serious among other prevailing cancer types. One of the reasons for it is that it is usually not diagnosed until it has spread and by that time it becomes very difficult to treat. Early detection of lung cancer can significantly increase the chances of survival of a cancer patient. An effective nodule detection system can play a key role in early detection of lung cancer thus increasing the chances of successful treatment. In this research work, we have proposed a novel classification framework for nodule classification. The framework consists of multiple phases that include image contrast enhancement, segmentation, optimal feature extraction, followed by employment of these features for training and testing of Support Vector Machine. We have empirically tested the efficacy of our technique by utilizing the well-known Lung Image Consortium Database (LIDC) dataset. The empirical results suggest that the technique is highly effective for reducing the false positive rates. We were able to receive an impressive sensitivity rate of 97.45%.
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Affiliation(s)
- Sajid Ali Khan
- Department of Software Engineering, Foundation University Islamabad, Islamabad, Pakistan.,Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan
| | - Shariq Hussain
- Department of Software Engineering, Foundation University Islamabad, Islamabad, Pakistan
| | - Shunkun Yang
- School of Reliability and Systems Engineering, Beihang University, Beijing, China.
| | - Khalid Iqbal
- COMSATS University Islamabad, Attock Campus, Attock, Pakistan
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16
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Improving Accuracy of Lung Nodule Classification Using Deep Learning with Focal Loss. JOURNAL OF HEALTHCARE ENGINEERING 2019; 2019:5156416. [PMID: 30863524 PMCID: PMC6378763 DOI: 10.1155/2019/5156416] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Accepted: 01/20/2019] [Indexed: 12/17/2022]
Abstract
Early detection and classification of pulmonary nodules using computer-aided diagnosis (CAD) systems is useful in reducing mortality rates of lung cancer. In this paper, we propose a new deep learning method to improve classification accuracy of pulmonary nodules in computed tomography (CT) scans. Our method uses a novel 15-layer 2D deep convolutional neural network architecture for automatic feature extraction and classification of pulmonary candidates as nodule or nonnodule. Focal loss function is then applied to the training process to boost classification accuracy of the model. We evaluated our method on the LIDC/IDRI dataset extracted by the LUNA16 challenge. The experiments showed that our deep learning method with focal loss is a high-quality classifier with an accuracy of 97.2%, sensitivity of 96.0%, and specificity of 97.3%.
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17
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Ye W, Gu W, Guo X, Yi P, Meng Y, Han F, Yu L, Chen Y, Zhang G, Wang X. Detection of pulmonary ground-glass opacity based on deep learning computer artificial intelligence. Biomed Eng Online 2019; 18:6. [PMID: 30670024 PMCID: PMC6343356 DOI: 10.1186/s12938-019-0627-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 01/16/2019] [Indexed: 01/15/2023] Open
Abstract
Background A deep learning computer artificial intelligence system is helpful for early identification of ground glass opacities (GGOs). Methods Images from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database were used in AlexNet and GoogLeNet to detect pulmonary nodules, and 221 GGO images provided by Xinhua Hospital were used in ResNet50 for detecting GGOs. We used computed tomography image radial reorganization to create the input image of the three-dimensional features, and used the extracted features for deep learning, network training, testing, and analysis. Results In the final evaluation results, we found that the accuracy of identification of lung nodule could reach 88.0%, with an F-score of 0.891. In terms of performance and accuracy, our method was better than the existing solutions. The GGO nodule classification achieved the best F-score of 0.87805. We propose a preprocessing method of red, green, and blue (RGB) superposition in the region of interest to effectively increase the differentiation between nodules and normal tissues, and that is the innovation of our research. Conclusions The method of deep learning proposed in this study is more sensitive than other systems in recent years, and the average false positive is lower than that of others.
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Affiliation(s)
- Wenjing Ye
- Department of Respiratory Medicine, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200092, China
| | - Wen Gu
- Department of Respiratory Medicine, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200092, China
| | - Xuejun Guo
- Department of Respiratory Medicine, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200092, China.
| | - Ping Yi
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Yishuang Meng
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Fengfeng Han
- Department of Respiratory Medicine, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200092, China
| | - Lingwei Yu
- Department of Radiology, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200092, China
| | - Yi Chen
- Department of Respiratory Medicine, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200092, China
| | - Guorui Zhang
- Department of Respiratory Medicine, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200092, China
| | - Xueting Wang
- Shanghai Jiaotong University School of Medicine, Shanghai, 200092, China
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Dandıl E. A Computer-Aided Pipeline for Automatic Lung Cancer Classification on Computed Tomography Scans. JOURNAL OF HEALTHCARE ENGINEERING 2018; 2018:9409267. [PMID: 30515286 PMCID: PMC6236771 DOI: 10.1155/2018/9409267] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Revised: 09/24/2018] [Accepted: 10/08/2018] [Indexed: 11/17/2022]
Abstract
Lung cancer is one of the most common cancer types. For the survival of the patient, early detection of lung cancer with the best treatment method is crucial. In this study, we propose a novel computer-aided pipeline on computed tomography (CT) scans for early diagnosis of lung cancer thanks to the classification of benign and malignant nodules. The proposed pipeline is composed of four stages. In preprocessing steps, CT images are enhanced, and lung volumes are extracted from the image with the help of a novel method called lung volume extraction method (LUVEM). The significance of the proposed pipeline is using LUVEM for extracting lung region. In nodule detection stage, candidate nodules are determined according to the circular Hough transform- (CHT-) based method. Then, lung nodules are segmented with self-organizing maps (SOM). In feature computation stage, intensity, shape, texture, energy, and combined features are used for feature extraction, and principal component analysis (PCA) is used for feature reduction step. In the final stage, probabilistic neural network (PNN) classifies benign and malign nodules. According to the experiments performed on our dataset, the proposed pipeline system can classify benign and malign nodules with 95.91% accuracy, 97.42% sensitivity, and 94.24% specificity. Even in cases of small-sized nodules (3-10 mm), the proposed system can determine the nodule type with 94.68% accuracy.
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Affiliation(s)
- Emre Dandıl
- Department of Computer Engineering, Faculty of Engineering, Bilecik Seyh Edebali University, Gulumbe Campus, 11210 Bilecik, Turkey
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19
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Detection and Classification of Overlapping Cell Nuclei in Cytology Effusion Images Using a Double-Strategy Random Forest. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8091608] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Due to the close resemblance between overlapping and cancerous nuclei, the misinterpretation of overlapping nuclei can affect the final decision of cancer cell detection. Thus, it is essential to detect overlapping nuclei and distinguish them from single ones for subsequent quantitative analyses. This paper presents a method for the automated detection and classification of overlapping nuclei from single nuclei appearing in cytology pleural effusion (CPE) images. The proposed system is comprised of three steps: nuclei candidate extraction, dominant feature extraction, and classification of single and overlapping nuclei. A maximum entropy thresholding method complemented by image enhancement and post-processing was employed for nuclei candidate extraction. For feature extraction, a new combination of 16 geometrical and 10 textural features was extracted from each nucleus region. A double-strategy random forest was performed as an ensemble feature selector to select the most relevant features, and an ensemble classifier to differentiate between overlapping nuclei and single ones using selected features. The proposed method was evaluated on 4000 nuclei from CPE images using various performance metrics. The results were 96.6% sensitivity, 98.7% specificity, 92.7% precision, 94.6% F1 score, 98.4% accuracy, 97.6% G-mean, and 99% area under curve. The computation time required to run the entire algorithm was just 5.17 s. The experiment results demonstrate that the proposed algorithm yields a superior performance to previous studies and other classifiers. The proposed algorithm can serve as a new supportive tool in the automated diagnosis of cancer cells from cytology images.
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20
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Saad M, Choi TS. Deciphering unclassified tumors of non-small-cell lung cancer through radiomics. Comput Biol Med 2018; 91:222-230. [PMID: 29100116 DOI: 10.1016/j.compbiomed.2017.10.029] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Revised: 10/24/2017] [Accepted: 10/24/2017] [Indexed: 01/14/2023]
Abstract
BACKGROUND Tumors are highly heterogeneous at the phenotypic, physiologic, and genomic levels. They are categorized in terms of a differentiated appearance under a microscope. Non-small-cell lung cancer tumors are categorized into three main subgroups: adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. In approximately 20% of pathology reports, they are returned unclassified or classified as not-otherwise-specified (NOS) owing to scant materials or poor tumor differentiation. METHOD We present a radiomic interrogation of molecular spatial variations to decode unclassified NOS tumor architecture quantitatively. Twelve spatial descriptors with various displacements and directions were extracted and profiled with respect to the subgroups. The profiled descriptors were used to decipher the NOS tumor morphologic clues from the imaging phenotype perspective. This profiler was built as an extended version of a conventional support-vector-machine classifier, wherein a genetic algorithm and correlation analysis were embedded to define the molecular signatures of poorly differentiated tumors using well-differentiated-tumor information. RESULTS Sixteen multi-class classifier models with an 81% average accuracy and descriptor subset size ranging from 12 to 144 were reported. The average area under the curve was 86.3% at a 95% confidence interval and a 0.03-0.08 standard error. Correlation analysis returned an unclassified NOS membership matrix with respect to the cell-architecture similarity score for the subgroups. The best model demonstrated 53% NOS reduction. CONCLUSION The membership matrix is expected to assist pathologists and oncologists in cases of unresectable tumors or scant biopsy materials for histological subtyping and cancer therapy.
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Affiliation(s)
- Maliazurina Saad
- School of Mechatronics, Gwangju Institute of Science and Technology, 123 Cheomdan-gwagiro, Oryong-Dong, Buk-gu, Gwangju 61005, South Korea.
| | - Tae-Sun Choi
- School of Mechatronics, Gwangju Institute of Science and Technology, 123 Cheomdan-gwagiro, Oryong-Dong, Buk-gu, Gwangju 61005, South Korea.
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21
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22
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Zia ur Rehman M, Javaid M, Shah SIA, Gilani SO, Jamil M, Butt SI. An appraisal of nodules detection techniques for lung cancer in CT images. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2017.11.017] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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23
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Setio AAA, Traverso A, de Bel T, Berens MS, Bogaard CVD, Cerello P, Chen H, Dou Q, Fantacci ME, Geurts B, Gugten RVD, Heng PA, Jansen B, de Kaste MM, Kotov V, Lin JYH, Manders JT, Sóñora-Mengana A, García-Naranjo JC, Papavasileiou E, Prokop M, Saletta M, Schaefer-Prokop CM, Scholten ET, Scholten L, Snoeren MM, Torres EL, Vandemeulebroucke J, Walasek N, Zuidhof GC, Ginneken BV, Jacobs C. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge. Med Image Anal 2017; 42:1-13. [PMID: 28732268 DOI: 10.1016/j.media.2017.06.015] [Citation(s) in RCA: 399] [Impact Index Per Article: 57.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2016] [Revised: 05/18/2017] [Accepted: 06/29/2017] [Indexed: 12/17/2022]
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24
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Shaukat F, Raja G, Gooya A, Frangi AF. Fully automatic detection of lung nodules in CT images using a hybrid feature set. Med Phys 2017; 44:3615-3629. [DOI: 10.1002/mp.12273] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2016] [Revised: 02/15/2017] [Accepted: 03/28/2017] [Indexed: 11/06/2022] Open
Affiliation(s)
- Furqan Shaukat
- Department of Electrical Engineering, University of Engineering & Technology, Taxila, 47080, Pakistan
| | - Gulistan Raja
- Department of Electrical Engineering, University of Engineering & Technology, Taxila, 47080, Pakistan
| | - Ali Gooya
- Department of Electronic and Electrical Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK
| | - Alejandro F Frangi
- Department of Electronic and Electrical Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK
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Liu JK, Jiang HY, Gao MD, He CG, Wang Y, Wang P, Ma H, Li Y. An Assisted Diagnosis System for Detection of Early Pulmonary Nodule in Computed Tomography Images. J Med Syst 2016; 41:30. [PMID: 28032305 DOI: 10.1007/s10916-016-0669-0] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2016] [Accepted: 12/07/2016] [Indexed: 11/28/2022]
Abstract
Lung cancer is still the most concerned disease around the world. Lung nodule generates in the pulmonary parenchyma which indicates the latent risk of lung cancer. Computer-aided pulmonary nodules detection system is necessary, which can reduce diagnosis time and decrease mortality of patients. In this study, we have proposed a new computer aided diagnosis (CAD) system for detection of early pulmonary nodule, which can help radiologists quickly locate suspected nodules and make judgments. This system consists of four main sections: pulmonary parenchyma segmentation, nodule candidate detection, features extraction (total 22 features) and nodule classification. The publicly available data set created by the Lung Image Database Consortium (LIDC) is used for training and testing. This study selects 6400 slices from 80 CT scans containing totally 978 nodules, which is labeled by four radiologists. Through a fast segmentation method proposed in this paper, pulmonary nodules including 888 true nodules and 11,379 false positive nodules are segmented. By means of an ensemble classifier, Random Forest (RF), this study acquires 93.2, 92.4, 94.8, 97.6% of accuracy, sensitivity, specificity, area under the curve (AUC), respectively. Compared with support vector machine (SVM) classifier, RF can reduce more false positive nodules and acquire larger AUC. With the help of this CAD system, radiologist can be provided with a great reference for pulmonary nodule diagnosis timely.
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Affiliation(s)
- Ji-Kui Liu
- Key Laboratory for Health Informatics of the Chinese Academy of Sciences (HICAS), Shenzhen Institutes of Advanced Technology, Shenzhen, 518055, Guangdong, China
| | - Hong-Yang Jiang
- Sino-Dutch Biomedical and Information Engineering School, Hunnan Campus, Northeastern University, Shenyang, 110169, Liaoning, China
| | - Meng-di Gao
- Sino-Dutch Biomedical and Information Engineering School, Hunnan Campus, Northeastern University, Shenyang, 110169, Liaoning, China
| | - Chen-Guang He
- Software School, North China University of Water Resources and Electric Power, Zhengzhou, 450045, Henan, China
| | - Yu Wang
- Sino-Dutch Biomedical and Information Engineering School, Hunnan Campus, Northeastern University, Shenyang, 110169, Liaoning, China
| | - Pu Wang
- Key Laboratory for Health Informatics of the Chinese Academy of Sciences (HICAS), Shenzhen Institutes of Advanced Technology, Shenzhen, 518055, Guangdong, China
| | - He Ma
- Sino-Dutch Biomedical and Information Engineering School, Hunnan Campus, Northeastern University, Shenyang, 110169, Liaoning, China.
| | - Ye Li
- Key Laboratory for Health Informatics of the Chinese Academy of Sciences (HICAS), Shenzhen Institutes of Advanced Technology, Shenzhen, 518055, Guangdong, China.
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26
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Setio AAA, Jacobs C, Gelderblom J, van Ginneken B. Automatic detection of large pulmonary solid nodules in thoracic CT images. Med Phys 2016; 42:5642-53. [PMID: 26429238 DOI: 10.1118/1.4929562] [Citation(s) in RCA: 94] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
PURPOSE Current computer-aided detection (CAD) systems for pulmonary nodules in computed tomography (CT) scans have a good performance for relatively small nodules, but often fail to detect the much rarer larger nodules, which are more likely to be cancerous. We present a novel CAD system specifically designed to detect solid nodules larger than 10 mm. METHODS The proposed detection pipeline is initiated by a three-dimensional lung segmentation algorithm optimized to include large nodules attached to the pleural wall via morphological processing. An additional preprocessing is used to mask out structures outside the pleural space to ensure that pleural and parenchymal nodules have a similar appearance. Next, nodule candidates are obtained via a multistage process of thresholding and morphological operations, to detect both larger and smaller candidates. After segmenting each candidate, a set of 24 features based on intensity, shape, blobness, and spatial context are computed. A radial basis support vector machine (SVM) classifier was used to classify nodule candidates, and performance was evaluated using ten-fold cross-validation on the full publicly available lung image database consortium database. RESULTS The proposed CAD system reaches a sensitivity of 98.3% (234/238) and 94.1% (224/238) large nodules at an average of 4.0 and 1.0 false positives/scan, respectively. CONCLUSIONS The authors conclude that the proposed dedicated CAD system for large pulmonary nodules can identify the vast majority of highly suspicious lesions in thoracic CT scans with a small number of false positives.
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Affiliation(s)
- Arnaud A A Setio
- Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands
| | - Colin Jacobs
- Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands
| | - Jaap Gelderblom
- Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands
| | - Bram van Ginneken
- Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands and Fraunhofer MEVIS, Bremen 28359, Germany
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27
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Lung Cancer Detection Using Fuzzy Auto-Seed Cluster Means Morphological Segmentation and SVM Classifier. J Med Syst 2016; 40:181. [DOI: 10.1007/s10916-016-0539-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2016] [Accepted: 06/08/2016] [Indexed: 10/21/2022]
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28
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Setio AAA, Ciompi F, Litjens G, Gerke P, Jacobs C, van Riel SJ, Wille MMW, Naqibullah M, Sanchez CI, van Ginneken B. Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:1160-1169. [PMID: 26955024 DOI: 10.1109/tmi.2016.2536809] [Citation(s) in RCA: 505] [Impact Index Per Article: 63.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
We propose a novel Computer-Aided Detection (CAD) system for pulmonary nodules using multi-view convolutional networks (ConvNets), for which discriminative features are automatically learnt from the training data. The network is fed with nodule candidates obtained by combining three candidate detectors specifically designed for solid, subsolid, and large nodules. For each candidate, a set of 2-D patches from differently oriented planes is extracted. The proposed architecture comprises multiple streams of 2-D ConvNets, for which the outputs are combined using a dedicated fusion method to get the final classification. Data augmentation and dropout are applied to avoid overfitting. On 888 scans of the publicly available LIDC-IDRI dataset, our method reaches high detection sensitivities of 85.4% and 90.1% at 1 and 4 false positives per scan, respectively. An additional evaluation on independent datasets from the ANODE09 challenge and DLCST is performed. We showed that the proposed multi-view ConvNets is highly suited to be used for false positive reduction of a CAD system.
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29
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Peña DM, Luo S, Abdelgader AMS. Auto Diagnostics of Lung Nodules Using Minimal Characteristics Extraction Technique. Diagnostics (Basel) 2016; 6:E13. [PMID: 26959065 PMCID: PMC4808828 DOI: 10.3390/diagnostics6010013] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2016] [Revised: 02/19/2016] [Accepted: 02/24/2016] [Indexed: 11/16/2022] Open
Abstract
Computer-aided detection (CAD) systems provide useful tools and an advantageous process to physicians aiming to detect lung nodules. This paper develops a method composed of four processes for lung nodule detection. The first step employs image acquisition and pre-processing techniques to isolate the lungs from the rest of the body. The second stage involves the segmentation process using a 2D algorithm to affect every layer of a scan eliminating non-informative structures inside the lungs, and a 3D blob algorithm associated with a connectivity algorithm to select possible nodule shape candidates. The combinations of these algorithms efficiently eliminate the high rates of false positives. The third process extracts eight minimal representative characteristics of the possible candidates. The final step utilizes a support vector machine for classifying the possible candidates into nodules and non-nodules depending on their features. As the objective is to find nodules bigger than 4mm, the proposed approach demonstrated quite encouraging results. Among 65 computer tomography (CT) scans, 94.23% of sensitivity and 84.75% in specificity were obtained. The accuracy of these two results was 89.19% taking into consideration that 45 scans were used for testing and 20 for training. The rate of false positives was 0.2 per scan.
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Affiliation(s)
- Diego M Peña
- Department of Digital Image Processing, Faculty of Biomedical Engineering, Southeast University, Nanjing 210096, China.
| | - Shouhua Luo
- Department of Digital Image Processing, Faculty of Biomedical Engineering, Southeast University, Nanjing 210096, China.
| | - Abdeldime M S Abdelgader
- Department of Digital Image Processing, Faculty of Biomedical Engineering, Southeast University, Nanjing 210096, China.
- Department of Electrical and Computer Engineering, Karary University, Khartoum 12304, Sudan.
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30
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Krishnamurthy S, Narasimhan G, Rengasamy U. Three-dimensional lung nodule segmentation and shape variance analysis to detect lung cancer with reduced false positives. Proc Inst Mech Eng H 2015; 230:58-70. [DOI: 10.1177/0954411915619951] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The three-dimensional analysis on lung computed tomography scan was carried out in this study to detect the malignant lung nodules. An automatic three-dimensional segmentation algorithm proposed here efficiently segmented the tissue clusters (nodules) inside the lung. However, an automatic morphological region-grow segmentation algorithm that was implemented to segment the well-circumscribed nodules present inside the lung did not segment the juxta-pleural nodule present on the inner surface of wall of the lung. A novel edge bridge and fill technique is proposed in this article to segment the juxta-pleural and pleural-tail nodules accurately. The centroid shift of each candidate nodule was computed. The nodules with more centroid shift in the consecutive slices were eliminated since malignant nodule’s resultant position did not usually deviate. The three-dimensional shape variation and edge sharp analyses were performed to reduce the false positives and to classify the malignant nodules. The change in area and equivalent diameter was more for malignant nodules in the consecutive slices and the malignant nodules showed a sharp edge. Segmentation was followed by three-dimensional centroid, shape and edge analysis which was carried out on a lung computed tomography database of 20 patient with 25 malignant nodules. The algorithms proposed in this article precisely detected 22 malignant nodules and failed to detect 3 with a sensitivity of 88%. Furthermore, this algorithm correctly eliminated 216 tissue clusters that were initially segmented as nodules; however, 41 non-malignant tissue clusters were detected as malignant nodules. Therefore, the false positive of this algorithm was 2.05 per patient.
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Affiliation(s)
| | - Ganesh Narasimhan
- Department of ECE, Rajalakshmi Institute of Technology, Chennai, India
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31
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Akram S, Javed MY, Hussain A, Riaz F, Usman Akram M. Intensity-based statistical features for classification of lungs CT scan nodules using artificial intelligence techniques. J EXP THEOR ARTIF IN 2015. [DOI: 10.1080/0952813x.2015.1020526] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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32
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Shen S, Bui AAT, Cong J, Hsu W. An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy. Comput Biol Med 2014; 57:139-49. [PMID: 25557199 DOI: 10.1016/j.compbiomed.2014.12.008] [Citation(s) in RCA: 73] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2014] [Revised: 12/06/2014] [Accepted: 12/10/2014] [Indexed: 11/18/2022]
Abstract
Computer-aided detection and diagnosis (CAD) has been widely investigated to improve radiologists׳ diagnostic accuracy in detecting and characterizing lung disease, as well as to assist with the processing of increasingly sizable volumes of imaging. Lung segmentation is a requisite preprocessing step for most CAD schemes. This paper proposes a parameter-free lung segmentation algorithm with the aim of improving lung nodule detection accuracy, focusing on juxtapleural nodules. A bidirectional chain coding method combined with a support vector machine (SVM) classifier is used to selectively smooth the lung border while minimizing the over-segmentation of adjacent regions. This automated method was tested on 233 computed tomography (CT) studies from the lung imaging database consortium (LIDC), representing 403 juxtapleural nodules. The approach obtained a 92.6% re-inclusion rate. Segmentation accuracy was further validated on 10 randomly selected CT series, finding a 0.3% average over-segmentation ratio and 2.4% under-segmentation rate when compared to manually segmented reference standards done by an expert.
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Affiliation(s)
- Shiwen Shen
- Department of Bioengineering, University of California, Los Angeles, CA, USA; Medical Imaging Informatics (MII) Group, Department of Radiological Sciences, University of California, Los Angeles, CA, USA.
| | - Alex A T Bui
- Medical Imaging Informatics (MII) Group, Department of Radiological Sciences, University of California, Los Angeles, CA, USA
| | - Jason Cong
- Department of Computer Science, University of California, Los Angeles, CA, USA
| | - William Hsu
- Medical Imaging Informatics (MII) Group, Department of Radiological Sciences, University of California, Los Angeles, CA, USA
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Choi WJ, Choi TS. Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2013; 113:37-54. [PMID: 24148147 DOI: 10.1016/j.cmpb.2013.08.015] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2012] [Revised: 08/22/2013] [Accepted: 08/23/2013] [Indexed: 06/02/2023]
Abstract
Computer-aided detection (CAD) can help radiologists to detect pulmonary nodules at an early stage. In pulmonary nodule CAD systems, feature extraction is very important for describing the characteristics of nodule candidates. In this paper, we propose a novel three-dimensional shape-based feature descriptor to detect pulmonary nodules in CT scans. After lung volume segmentation, nodule candidates are detected using multi-scale dot enhancement filtering in the segmented lung volume. Next, we extract feature descriptors from the detected nodule candidates, and these are refined using an iterative wall elimination method. Finally, a support vector machine-based classifier is trained to classify nodules and non-nodules. The performance of the proposed system is evaluated on Lung Image Database Consortium data. The proposed method significantly reduces the number of false positives in nodule candidates. This method achieves 97.5% sensitivity, with only 6.76 false positives per scan.
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Affiliation(s)
- Wook-Jin Choi
- Gwangju Institute of Science and Technology (GIST), School of Information and Mechatronics, 123 Cheomdan-gwagiro, Buk-Gu, Gwangju 500-712, Republic of Korea(1).
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