1
|
Song W, Tang F, Marshall H, Fong KM, Liu F. A multiscale 3D network for lung nodule detection using flexible nodule modeling. Med Phys 2024. [PMID: 38949577 DOI: 10.1002/mp.17283] [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: 01/19/2024] [Revised: 05/17/2024] [Accepted: 06/18/2024] [Indexed: 07/02/2024] Open
Abstract
BACKGROUND Lung cancer is the most common type of cancer. Detection of lung cancer at an early stage can reduce mortality rates. Pulmonary nodules may represent early cancer and can be identified through computed tomography (CT) scans. Malignant risk can be estimated based on attributes like size, shape, location, and density. PURPOSE Deep learning algorithms have achieved remarkable advancements in this domain compared to traditional machine learning methods. Nevertheless, many existing anchor-based deep learning algorithms exhibit sensitivity to predefined anchor-box configurations, necessitating manual adjustments to obtain optimal outcomes. Conversely, current anchor-free deep learning-based nodule detection methods normally adopt fixed-size nodule models like cubes or spheres. METHODS To address these technical challenges, we propose a multiscale 3D anchor-free deep learning network (M3N) for pulmonary nodule detection, leveraging adjustable nodule modeling (ANM). Within this framework, ANM empowers the representation of target objects in an anisotropic manner, with a novel point selection strategy (PSS) devised to accelerate the learning process of anisotropic representation. We further incorporate a composite loss function that combines the conventional L2 loss and cosine similarity loss, facilitating M3N to learn nodules' intensity distribution in three dimensions. RESULTS Experiment results show that the M3N achieves 90.6% competitive performance metrics (CPM) with seven predefined false positives per scan on the LUNA 16 dataset. This performance appears to exceed that of other state-of-the-art deep learning-based networks reported in their respective publications. Individual test results also demonstrate that M3N excels in providing more accurate, adaptive bounding boxes surrounding the contours of target nodules. CONCLUSIONS The newly developed nodule detection system reduces reliance on prior knowledge, such as the general size of objects in the dataset, thus it should enhance overall robustness and versatility. Distinct from traditional nodule modeling techniques, the ANM approach aligns more closely with the morphological characteristics of nodules. Time consumption and detection results demonstrate promising efficiency and accuracy which should be validated in clinical settings.
Collapse
Affiliation(s)
- Wenjia Song
- School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Australia
| | - Fangfang Tang
- School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Australia
| | - Henry Marshall
- UQ Thoracic Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia
- Department of Thoracic Medicine, The Prince Charles Hospital, Brisbane, Australia
| | - Kwun M Fong
- UQ Thoracic Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia
- Department of Thoracic Medicine, The Prince Charles Hospital, Brisbane, Australia
| | - Feng Liu
- School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Australia
| |
Collapse
|
2
|
CJT-DEO: Condorcet’s Jury Theorem and Differential Evolution Optimization based ensemble of deep neural networks for pulmonary and colorectal cancer classification. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
|
3
|
Fahmy D, Kandil H, Khelifi A, Yaghi M, Ghazal M, Sharafeldeen A, Mahmoud A, El-Baz A. How AI Can Help in the Diagnostic Dilemma of Pulmonary Nodules. Cancers (Basel) 2022; 14:cancers14071840. [PMID: 35406614 PMCID: PMC8997734 DOI: 10.3390/cancers14071840] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 03/29/2022] [Accepted: 03/30/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary Pulmonary nodules are considered a sign of bronchogenic carcinoma, detecting them early will reduce their progression and can save lives. Lung cancer is the second most common type of cancer in both men and women. This manuscript discusses the current applications of artificial intelligence (AI) in lung segmentation as well as pulmonary nodule segmentation and classification using computed tomography (CT) scans, published in the last two decades, in addition to the limitations and future prospects in the field of AI. Abstract Pulmonary nodules are the precursors of bronchogenic carcinoma, its early detection facilitates early treatment which save a lot of lives. Unfortunately, pulmonary nodule detection and classification are liable to subjective variations with high rate of missing small cancerous lesions which opens the way for implementation of artificial intelligence (AI) and computer aided diagnosis (CAD) systems. The field of deep learning and neural networks is expanding every day with new models designed to overcome diagnostic problems and provide more applicable and simply used models. We aim in this review to briefly discuss the current applications of AI in lung segmentation, pulmonary nodule detection and classification.
Collapse
Affiliation(s)
- Dalia Fahmy
- Diagnostic Radiology Department, Mansoura University Hospital, Mansoura 35516, Egypt;
| | - Heba Kandil
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (H.K.); (A.S.); (A.M.)
- Information Technology Department, Faculty of Computers and Informatics, Mansoura University, Mansoura 35516, Egypt
| | - Adel Khelifi
- Computer Science and Information Technology Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates;
| | - Maha Yaghi
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.Y.); (M.G.)
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.Y.); (M.G.)
| | - Ahmed Sharafeldeen
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (H.K.); (A.S.); (A.M.)
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (H.K.); (A.S.); (A.M.)
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (H.K.); (A.S.); (A.M.)
- Correspondence:
| |
Collapse
|
4
|
Niu H, Gu J. RETRACTED ARTICLE: AI driven feature extraction model for chest cavity spectrum signal visualization. INTERNATIONAL JOURNAL OF SPEECH TECHNOLOGY 2022; 25:7. [PMID: 33967593 PMCID: PMC8090519 DOI: 10.1007/s10772-021-09844-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 04/04/2021] [Indexed: 05/11/2023]
Affiliation(s)
- Haitao Niu
- School of Physical Science and Technology, Soochow University, Suzhou, 215006 China
| | - Jihua Gu
- School of Optoelectronic Science and Engineering, Soochow University, Suzhou, 215006 China
| |
Collapse
|
5
|
Liew WS, Tang TB, Lin CH, Lu CK. Automatic colonic polyp detection using integration of modified deep residual convolutional neural network and ensemble learning approaches. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 206:106114. [PMID: 33984661 DOI: 10.1016/j.cmpb.2021.106114] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 04/07/2021] [Indexed: 05/10/2023]
Abstract
BACKGROUND AND OBJECTIVE The increased incidence of colorectal cancer (CRC) and its mortality rate have attracted interest in the use of artificial intelligence (AI) based computer-aided diagnosis (CAD) tools to detect polyps at an early stage. Although these CAD tools have thus far achieved a good accuracy level to detect polyps, they still have room to improve further (e.g. sensitivity). Therefore, a new CAD tool is developed in this study to detect colonic polyps accurately. METHODS In this paper, we propose a novel approach to distinguish colonic polyps by integrating several techniques, including a modified deep residual network, principal component analysis and AdaBoost ensemble learning. A powerful deep residual network architecture, ResNet-50, was investigated to reduce the computational time by altering its architecture. To keep the interference to a minimum, median filter, image thresholding, contrast enhancement, and normalisation techniques were exploited on the endoscopic images to train the classification model. Three publicly available datasets, i.e., Kvasir, ETIS-LaribPolypDB, and CVC-ClinicDB, were merged to train the model, which included images with and without polyps. RESULTS The proposed approach trained with a combination of three datasets achieved Matthews Correlation Coefficient (MCC) of 0.9819 with accuracy, sensitivity, precision, and specificity of 99.10%, 98.82%, 99.37%, and 99.38%, respectively. CONCLUSIONS These results show that our method could repeatedly classify endoscopic images automatically and could be used to effectively develop computer-aided diagnostic tools for early CRC detection.
Collapse
Affiliation(s)
- Win Sheng Liew
- Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak, Malaysia
| | - Tong Boon Tang
- Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak, Malaysia
| | - Cheng-Hung Lin
- Department of Electrical Engineering and Biomedical Engineering Research Center, Yuan Ze University, Jungli 32003, Taiwan
| | - Cheng-Kai Lu
- Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak, Malaysia.
| |
Collapse
|
6
|
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
| |
Collapse
|
7
|
Ren Y, Ma J, Xiong J, Chen Y, Lu L, Zhao J. Improved False Positive Reduction by Novel Morphological Features for Computer-Aided Polyp Detection in CT Colonography. IEEE J Biomed Health Inform 2019; 23:324-333. [DOI: 10.1109/jbhi.2018.2808199] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
|
8
|
Tan J, Huo Y, Liang Z, Li L. Expert knowledge-infused deep learning for automatic lung nodule detection. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2019; 27:17-35. [PMID: 30452432 PMCID: PMC6453714 DOI: 10.3233/xst-180426] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
BACKGROUND Computer aided detection (CADe) of pulmonary nodules from computed tomography (CT) is crucial for early diagnosis of lung cancer. Self-learned features obtained by training datasets via deep learning have facilitated CADe of the nodules. However, the complexity of CT lung images renders a challenge of extracting effective features by self-learning only. This condition is exacerbated for limited size of datasets. On the other hand, the engineered features have been widely studied. OBJECTIVE We proposed a novel nodule CADe which aims to relieve the challenge by the use of available engineered features to prevent convolution neural networks (CNN) from overfitting under dataset limitation and reduce the running-time complexity of self-learning. METHODS The CADe methodology infuses adequately the engineered features, particularly texture features, into the deep learning process. RESULTS The methodology was validated on 208 patients with at least one juxta-pleural nodule from the public LIDC-IDRI database. Results demonstrated that the methodology achieves a sensitivity of 88% with 1.9 false positives per scan and a sensitivity of 94.01% with 4.01 false positives per scan. CONCLUSIONS The methodology shows high performance compared with the state-of-the-art results, in terms of accuracy and efficiency, from both existing CNN-based approaches and engineered feature-based classifications.
Collapse
Affiliation(s)
- Jiaxing Tan
- Department of Computer Science, City University of New York, the Graduate Center, NY, USA
| | - Yumei Huo
- Department of Computer Science, City University of New York at CSI, NY, USA
| | - Zhengrong Liang
- Department of Radiology, State University of New York at Stony Brook, NY, USA
- Corresponding author: Zhengrong Liang, Department of Radiology, Electrical and Computer Engineering, and Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA. .
| | - Lihong Li
- Department of Engineering Science and Physics, City University of New York at CSI, NY, USA
| |
Collapse
|
9
|
Larsen TC, Gopalakrishnan V, Yao J, Nguyen CP, Chen MY, Moss J, Wen H. Optimization of a secondary VOI protocol for lung imaging in a clinical CT scanner. J Appl Clin Med Phys 2018; 19:271-280. [PMID: 29785839 PMCID: PMC6036356 DOI: 10.1002/acm2.12354] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Revised: 04/02/2018] [Accepted: 04/10/2018] [Indexed: 11/10/2022] Open
Abstract
We present a solution to meet an unmet clinical need of an in-situ "close look" at a pulmonary nodule or at the margins of a pulmonary cyst revealed by a primary (screening) chest CT while the patient is still in the scanner. We first evaluated options available on current whole-body CT scanners for high resolution screening scans, including ROI reconstruction of the primary scan data and HRCT, but found them to have insufficient SNR in lung tissue or discontinuous slice coverage. Within the capabilities of current clinical CT systems, we opted for the solution of a secondary, volume-of-interest (VOI) protocol where the radiation dose is focused into a short-beam axial scan at the z position of interest, combined with a small-FOV reconstruction at the xy position of interest. The objective of this work was to design a VOI protocol that is optimized for targeted lung imaging in a clinical whole-body CT system. Using a chest phantom containing a lung-mimicking foam insert with a simulated cyst, we identified the appropriate scan mode and optimized both the scan and recon parameters. The VOI protocol yielded 3.2 times the texture amplitude-to-noise ratio in the lung-mimicking foam when compared to the standard chest CT, and 8.4 times the texture difference between the lung mimicking and reference foams. It improved details of the wall of the simulated cyst and better resolution in a line-pair insert. The Effective Dose of the secondary VOI protocol was 42% on average and up to 100% in the worst-case scenario of VOI positioning relative to the standard chest CT. The optimized protocol will be used to obtain detailed CT textures of pulmonary lesions, which are biomarkers for the type and stage of lung diseases.
Collapse
Affiliation(s)
- Thomas C Larsen
- National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Vissagan Gopalakrishnan
- National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA.,Rush Medical College, Chicago, IL, USA
| | - Jianhua Yao
- Department of Radiology, Hatfield Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Catherine P Nguyen
- National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Marcus Y Chen
- National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Joel Moss
- National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Han Wen
- National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| |
Collapse
|
10
|
Zhang X, Hu W, Chen F, Liu J, Yang Y, Wang L, Duan H, Si J. Gastric precancerous diseases classification using CNN with a concise model. PLoS One 2017; 12:e0185508. [PMID: 28950010 PMCID: PMC5614663 DOI: 10.1371/journal.pone.0185508] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2017] [Accepted: 09/14/2017] [Indexed: 12/18/2022] Open
Abstract
Gastric precancerous diseases (GPD) may deteriorate into early gastric cancer if misdiagnosed, so it is important to help doctors recognize GPD accurately and quickly. In this paper, we realize the classification of 3-class GPD, namely, polyp, erosion, and ulcer using convolutional neural networks (CNN) with a concise model called the Gastric Precancerous Disease Network (GPDNet). GPDNet introduces fire modules from SqueezeNet to reduce the model size and parameters about 10 times while improving speed for quick classification. To maintain classification accuracy with fewer parameters, we propose an innovative method called iterative reinforced learning (IRL). After training GPDNet from scratch, we apply IRL to fine-tune the parameters whose values are close to 0, and then we take the modified model as a pretrained model for the next training. The result shows that IRL can improve the accuracy about 9% after 6 iterations. The final classification accuracy of our GPDNet was 88.90%, which is promising for clinical GPD recognition.
Collapse
Affiliation(s)
- Xu Zhang
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Biomedical Engineering, Ministry of Education, Zhejiang University, Hangzhou, China
| | - Weiling Hu
- Department of Gastroenterology, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, China
- Institute of Gastroenterology, Zhejiang University, Hangzhou, China
| | - Fei Chen
- Institute of Gastroenterology, Zhejiang University, Hangzhou, China
- Department of Gastroenterology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Jiquan Liu
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Biomedical Engineering, Ministry of Education, Zhejiang University, Hangzhou, China
| | - Yuanhang Yang
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Biomedical Engineering, Ministry of Education, Zhejiang University, Hangzhou, China
| | - Liangjing Wang
- Institute of Gastroenterology, Zhejiang University, Hangzhou, China
- Department of Gastroenterology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Huilong Duan
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
- Key Laboratory of Biomedical Engineering, Ministry of Education, Zhejiang University, Hangzhou, China
| | - Jianmin Si
- Department of Gastroenterology, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, China
- Institute of Gastroenterology, Zhejiang University, Hangzhou, China
| |
Collapse
|
11
|
Zhu H, Pak CH, Song C, Dou S, Zhao H, Cao P, Ye X. A novel lung cancer detection algorithm for CADs based on SSP and Level Set. Technol Health Care 2017; 25:345-355. [PMID: 28582923 DOI: 10.3233/thc-171338] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The fuzzy degree of lung nodule boundary is the most important cue to judge the lung cancer in CT images. Based on this feature, the paper proposes a novel lung cancer detection method for CT images based on the super-pixels and the level set segmentation methods. In the proposed methods, the super-pixels method is used to segment the lung region and the suspected lung cancer lesion region in the CT image. The super-pixels method and a level set method are used to segment the suspected lung cancer lesion region simultaneously. Finally, the cancer is determined by the difference between results of the two segmentation methods. Experimental results show that the proposed algorithm has a high accuracy for lung cancer detection in CT images. For gross glass nodule, pleural nodule, the vascular nodules and solitary nodules, the sensitivity of the detection algorithm are respectively 91.3%, 96.3%, 80.9% and 82.3%.
Collapse
Affiliation(s)
- Hongbo Zhu
- School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Chun-Hyok Pak
- School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China.,Electronic Engineering faculty, Kim Chaek University of Technology, Pyongyang 999093, DPRK
| | - Chunhe Song
- Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, China
| | - Shengchang Dou
- School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Hai Zhao
- School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Peng Cao
- School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China.,School of Public Foundation, China Medical University, Shenyang, Liaoning, China
| | - Xiangyun Ye
- Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
| |
Collapse
|
12
|
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
| |
Collapse
|
13
|
A CAD of fully automated colonic polyp detection for contrasted and non-contrasted CT scans. Int J Comput Assist Radiol Surg 2017; 12:627-644. [PMID: 28101760 DOI: 10.1007/s11548-017-1521-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2016] [Accepted: 01/04/2017] [Indexed: 10/20/2022]
Abstract
PURPOSE Computer-aided detection (CAD) systems are developed to help radiologists detect colonic polyps over CT scans. It is possible to reduce the detection time and increase the detection accuracy rates by using CAD systems. In this paper, we aimed to develop a fully integrated CAD system for automated detection of polyps that yields a high polyp detection rate with a reasonable number of false positives. METHODS The proposed CAD system is a multistage implementation whose main components are: automatic colon segmentation, candidate detection, feature extraction and classification. The first element of the algorithm includes a discrete segmentation for both air and fluid regions. Colon-air regions were determined based on adaptive thresholding, and the volume/length measure was used to detect air regions. To extract the colon-fluid regions, a rule-based connectivity test was used to detect the regions belong to the colon. Potential polyp candidates were detected based on the 3D Laplacian of Gaussian filter. The geometrical features were used to reduce false-positive detections. A 2D projection image was generated to extract discriminative features as the inputs of an artificial neural network classifier. RESULTS Our CAD system performs at 100% sensitivity for polyps larger than 9 mm, 95.83% sensitivity for polyps 6-10 mm and 85.71% sensitivity for polyps smaller than 6 mm with 5.3 false positives per dataset. Also, clinically relevant polyps ([Formula: see text]6 mm) were identified with 96.67% sensitivity at 1.12 FP/dataset. CONCLUSIONS To the best of our knowledge, the novel polyp candidate detection system which determines polyp candidates with LoG filters is one of the main contributions. We also propose a new 2D projection image calculation scheme to determine the distinctive features. We believe that our CAD system is highly effective for assisting radiologist interpreting CT.
Collapse
|
14
|
|
15
|
Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2016; 2016:6215085. [PMID: 28070212 PMCID: PMC5192289 DOI: 10.1155/2016/6215085] [Citation(s) in RCA: 104] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Revised: 11/04/2016] [Accepted: 11/22/2016] [Indexed: 01/06/2023]
Abstract
Computer aided detection (CAD) systems can assist radiologists by offering a second opinion on early diagnosis of lung cancer. Classification and feature representation play critical roles in false-positive reduction (FPR) in lung nodule CAD. We design a deep convolutional neural networks method for nodule classification, which has an advantage of autolearning representation and strong generalization ability. A specified network structure for nodule images is proposed to solve the recognition of three types of nodules, that is, solid, semisolid, and ground glass opacity (GGO). Deep convolutional neural networks are trained by 62,492 regions-of-interest (ROIs) samples including 40,772 nodules and 21,720 nonnodules from the Lung Image Database Consortium (LIDC) database. Experimental results demonstrate the effectiveness of the proposed method in terms of sensitivity and overall accuracy and that it consistently outperforms the competing methods.
Collapse
|
16
|
Ren Y, Ma J, Xiong J, Lu L, Zhao J. High-Performance CAD-CTC Scheme Using Shape Index, Multiscale Enhancement Filters, and Radiomic Features. IEEE Trans Biomed Eng 2016; 64:1924-1934. [PMID: 27893377 DOI: 10.1109/tbme.2016.2631245] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Computer-aided detection (CAD) systems for computed tomography colonography (CTC) can automatically detect colorectal polyps. The main problem of currently developed CAD-CTC systems is the numerous false positives (FPs) caused by the existence of complicated colon structures (e.g., haustral fold, residual fecal material, inflation tube, and ileocecal valve). This study proposes a CAD-CTC scheme using shape index, multiscale enhancement filters, and radiomic features to address the FP issue. METHODS Shape index and multiscale enhancement filter calculated in the Gaussian smoothed geodesic distance field are combined to generate the polyp candidates. A total of 440 well-defined radiomic features collected from previous radiomic studies and 200 newly developed radiomic features are used to construct a supervised classification model to reduce the numerous FPs. RESULTS The proposed CAD-CTC scheme was evaluated on 152 oral contrast-enhanced CT datasets from 76 patients with 103 polyps ≥5 mm. The detection results were 98.1% and 95.3% by-polyp sensitivity and per-scan sensitivity, respectively, with the same FP rate of 1.3 FPs per dataset for polyps ≥5 mm. CONCLUSION Experimental results indicate that the proposed CAD-CTC scheme can achieve high sensitivity while maintaining a low FP rate. SIGNIFICANCE The proposed CAD-CTC scheme would be a beneficial tool in clinical colon examination.
Collapse
|
17
|
Fuzzy Clustering Applied to ROI Detection in Helical Thoracic CT Scans with a New Proposal and Variants. BIOMED RESEARCH INTERNATIONAL 2016; 2016:8058245. [PMID: 27517049 PMCID: PMC4967987 DOI: 10.1155/2016/8058245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2015] [Accepted: 06/09/2016] [Indexed: 11/17/2022]
Abstract
The detection of pulmonary nodules is one of the most studied problems in the field of medical image analysis due to the great difficulty in the early detection of such nodules and their social impact. The traditional approach involves the development of a multistage CAD system capable of informing the radiologist of the presence or absence of nodules. One stage in such systems is the detection of ROI (regions of interest) that may be nodules in order to reduce the space of the problem. This paper evaluates fuzzy clustering algorithms that employ different classification strategies to achieve this goal. After characterising these algorithms, the authors propose a new algorithm and different variations to improve the results obtained initially. Finally it is shown as the most recent developments in fuzzy clustering are able to detect regions that may be nodules in CT studies. The algorithms were evaluated using helical thoracic CT scans obtained from the database of the LIDC (Lung Image Database Consortium).
Collapse
|
18
|
Lu L, Tan Y, Schwartz LH, Zhao B. Hybrid detection of lung nodules on CT scan images. Med Phys 2016; 42:5042-54. [PMID: 26328955 DOI: 10.1118/1.4927573] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE The diversity of lung nodules poses difficulty for the current computer-aided diagnostic (CAD) schemes for lung nodule detection on computed tomography (CT) scan images, especially in large-scale CT screening studies. We proposed a novel CAD scheme based on a hybrid method to address the challenges of detection in diverse lung nodules. METHODS The hybrid method proposed in this paper integrates several existing and widely used algorithms in the field of nodule detection, including morphological operation, dot-enhancement based on Hessian matrix, fuzzy connectedness segmentation, local density maximum algorithm, geodesic distance map, and regression tree classification. All of the adopted algorithms were organized into tree structures with multi-nodes. Each node in the tree structure aimed to deal with one type of lung nodule. RESULTS The method has been evaluated on 294 CT scans from the Lung Image Database Consortium (LIDC) dataset. The CT scans were randomly divided into two independent subsets: a training set (196 scans) and a test set (98 scans). In total, the 294 CT scans contained 631 lung nodules, which were annotated by at least two radiologists participating in the LIDC project. The sensitivity and false positive per scan for the training set were 87% and 2.61%. The sensitivity and false positive per scan for the testing set were 85.2% and 3.13%. CONCLUSIONS The proposed hybrid method yielded high performance on the evaluation dataset and exhibits advantages over existing CAD schemes. We believe that the present method would be useful for a wide variety of CT imaging protocols used in both routine diagnosis and screening studies.
Collapse
Affiliation(s)
- Lin Lu
- Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, New York 10032
| | - Yongqiang Tan
- Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, New York 10032
| | - Lawrence H Schwartz
- Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, New York 10032
| | - Binsheng Zhao
- Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, New York 10032
| |
Collapse
|
19
|
Teramoto A, Fujita H, Yamamuro O, Tamaki T. Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique. Med Phys 2016; 43:2821-2827. [DOI: 10.1118/1.4948498] [Citation(s) in RCA: 156] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
|
20
|
Valente IRS, Cortez PC, Neto EC, Soares JM, de Albuquerque VHC, Tavares JMRS. Automatic 3D pulmonary nodule detection in CT images: A survey. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 124:91-107. [PMID: 26652979 DOI: 10.1016/j.cmpb.2015.10.006] [Citation(s) in RCA: 102] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2015] [Revised: 09/01/2015] [Accepted: 10/03/2015] [Indexed: 06/05/2023]
Abstract
This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized-tomography (CT) images. Its main goals are to analyze the latest technology being used for the development of computational diagnostic tools to assist in the acquisition, storage and, mainly, processing and analysis of the biomedical data. Also, this work identifies the progress made, so far, evaluates the challenges to be overcome and provides an analysis of future prospects. As far as the authors know, this is the first time that a review is devoted exclusively to automated 3D techniques for the detection of pulmonary nodules from lung CT images, which makes this work of noteworthy value. The research covered the published works in the Web of Science, PubMed, Science Direct and IEEEXplore up to December 2014. Each work found that referred to automated 3D segmentation of the lungs was individually analyzed to identify its objective, methodology and results. Based on the analysis of the selected works, several studies were seen to be useful for the construction of medical diagnostic aid tools. However, there are certain aspects that still require attention such as increasing algorithm sensitivity, reducing the number of false positives, improving and optimizing the algorithm detection of different kinds of nodules with different sizes and shapes and, finally, the ability to integrate with the Electronic Medical Record Systems and Picture Archiving and Communication Systems. Based on this analysis, we can say that further research is needed to develop current techniques and that new algorithms are needed to overcome the identified drawbacks.
Collapse
Affiliation(s)
- Igor Rafael S Valente
- Instituto Federal do Ceará, Campus Maracanaú, Av. Parque Central, S/N, Distrito Industrial I, 61939-140 Maracanaú, Ceará, Brazil; Universidade Federal do Ceará, Departamento de Engenharia de Teleinformática, Av. Mister Hull, S/N, Campus do Pici, 6005, 60455-760 Fortaleza, Ceará, Brazil
| | - Paulo César Cortez
- Universidade Federal do Ceará, Departamento de Engenharia de Teleinformática, Av. Mister Hull, S/N, Campus do Pici, 6005, 60455-760 Fortaleza, Ceará, Brazil
| | - Edson Cavalcanti Neto
- Universidade Federal do Ceará, Departamento de Engenharia de Teleinformática, Av. Mister Hull, S/N, Campus do Pici, 6005, 60455-760 Fortaleza, Ceará, Brazil
| | - José Marques Soares
- Universidade Federal do Ceará, Departamento de Engenharia de Teleinformática, Av. Mister Hull, S/N, Campus do Pici, 6005, 60455-760 Fortaleza, Ceará, Brazil
| | - Victor Hugo C de Albuquerque
- Programa de Pós-Graduacão em Informática Aplicada, Universidade de Fortaleza, Av. Washington Soares, 1321, Edson Queiroz, 60811341, CEP 608113-41 Fortaleza, Ceará, Brazil
| | - João Manuel R S Tavares
- Instituto de Ciência e Inovacão em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, S/N, 4200-465 Porto, Portugal.
| |
Collapse
|
21
|
Song J, Yang C, Fan L, Wang K, Yang F, Liu S, Tian J. Lung Lesion Extraction Using a Toboggan Based Growing Automatic Segmentation Approach. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:337-353. [PMID: 26336121 DOI: 10.1109/tmi.2015.2474119] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The accurate segmentation of lung lesions from computed tomography (CT) scans is important for lung cancer research and can offer valuable information for clinical diagnosis and treatment. However, it is challenging to achieve a fully automatic lesion detection and segmentation with acceptable accuracy due to the heterogeneity of lung lesions. Here, we propose a novel toboggan based growing automatic segmentation approach (TBGA) with a three-step framework, which are automatic initial seed point selection, multi-constraints 3D lesion extraction and the final lesion refinement. The new approach does not require any human interaction or training dataset for lesion detection, yet it can provide a high lesion detection sensitivity (96.35%) and a comparable segmentation accuracy with manual segmentation (P > 0.05), which was proved by a series assessments using the LIDC-IDRI dataset (850 lesions) and in-house clinical dataset (121 lesions). We also compared TBGA with commonly used level set and skeleton graph cut methods, respectively. The results indicated a significant improvement of segmentation accuracy . Furthermore, the average time consumption for one lesion segmentation was under 8 s using our new method. In conclusion, we believe that the novel TBGA can achieve robust, efficient and accurate lung lesion segmentation in CT images automatically.
Collapse
|
22
|
Wang H, Liang Z, Li LC, Han H, Song B, Pickhardt PJ, Barish MA, Lascarides CE. An adaptive paradigm for computer-aided detection of colonic polyps. Phys Med Biol 2015; 60:7207-28. [PMID: 26348125 PMCID: PMC4565750 DOI: 10.1088/0031-9155/60/18/7207] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Most previous efforts in developing computer-aided detection (CADe) of colonic polyps apply similar measures or parameters to detect polyps regardless of their locations under an implicit assumption that all the polyps reside in a similar local environment, e.g. on a relatively flat colon wall. In reality, this implicit assumption is frequently invalid, because the haustral folds can have a very different local environment from that of the relatively flat colon wall. We conjecture that this assumption may be a major cause of missing the detection of polyps, especially small polyps (<10 mm linear size) located on the haustral folds. In this paper, we take the concept of adaptiveness and present an adaptive paradigm for CADe of colonic polyps. Firstly, we decompose the complicated colon structure into two simplified sub-structures, each of which has similar properties, of (1) relatively flat colon wall and (2) ridge-shaped haustral folds. Then we develop local environment descriptions to adaptively reflect each of these two simplified sub-structures. To show the impact of the adaptiveness of the local environment descriptions upon the polyp detection task, we focus on the local geometrical measures of the volume data for both the detection of initial polyp candidates (IPCs) and the reduction of false positives (FPs) in the IPC pool. The experimental outcome using the local geometrical measures is very impressive such that not only the previously-missed small polyps on the folds are detected, but also the previously miss-removed small polyps on the folds during FP reduction are retained. It is expected that this adaptive paradigm will have a great impact on detecting the small polyps, measuring their volumes and volume changes over time, and optimizing their management plan.
Collapse
Affiliation(s)
- Huafeng Wang
- Dept. of Radiology, State Univ. of New York, Stony Brook, NY 11794, USA
- School of Software, Beihang Univ., Beijing 10083, China
| | - Zhengrong Liang
- Dept. of Radiology, State Univ. of New York, Stony Brook, NY 11794, USA
| | - Lihong C. Li
- Dept. of Engineering Science & Physics, City Univ. of New York, Staten Island, NY 10314, USA
| | - Hao Han
- Dept. of Radiology, State Univ. of New York, Stony Brook, NY 11794, USA
| | - Bowen Song
- Dept. of Radiology, State Univ. of New York, Stony Brook, NY 11794, USA
| | - Perry J. Pickhardt
- Dept. of Radiology, Univ. of Wisconsin Medical School, Madison, WI 53792, USA
| | - Matthew A. Barish
- Dept. of Radiology, State Univ. of New York, Stony Brook, NY 11794, USA
| | | |
Collapse
|
23
|
Pulmonary Nodule Detection from X-ray CT Images Based on Region Shape Analysis and Appearance-based Clustering. ALGORITHMS 2015. [DOI: 10.3390/a8020209] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
24
|
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]
|
25
|
Taşcı E, Uğur A. Shape and texture based novel features for automated juxtapleural nodule detection in lung CTs. J Med Syst 2015; 39:46. [PMID: 25732079 DOI: 10.1007/s10916-015-0231-5] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Accepted: 02/11/2015] [Indexed: 10/23/2022]
Abstract
Lung cancer is one of the types of cancer with highest mortality rate in the world. In case of early detection and diagnosis, the survival rate of patients significantly increases. In this study, a novel method and system that provides automatic detection of juxtapleural nodule pattern have been developed from cross-sectional images of lung CT (Computerized Tomography). Shape-based and both shape and texture based 7 features are contributed to the literature for lung nodules. System that we developed consists of six main stages called preprocessing, lung segmentation, detection of nodule candidate regions, feature extraction, feature selection (with five feature ranking criteria) and classification. LIDC dataset containing cross-sectional images of lung CT has been utilized, 1410 nodule candidate regions and 40 features have been extracted from 138 cross-sectional images for 24 patients. Experimental results for 10 classifiers are obtained and presented. Adding our derived features to known 33 features has increased nodule recognition performance from 0.9639 to 0.9679 AUC value on generalized linear model regression (GLMR) for 22 selected features and being reached one of the most successful results in the literature.
Collapse
Affiliation(s)
- Erdal Taşcı
- Department of Computer Engineering, Ege University, Izmir, Turkey,
| | | |
Collapse
|
26
|
Wang B, Tian X, Wang Q, Yang Y, Xie H, Zhang S, Gu L. Pulmonary nodule detection in CT images based on shape constraint CV model. Med Phys 2015; 42:1241-54. [DOI: 10.1118/1.4907961] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
|
27
|
Lu Y, Chen L, Gindi G. Collimator performance evaluation for In-111 SPECT using a detection/localization task. Phys Med Biol 2014; 59:679-96. [PMID: 24442348 DOI: 10.1088/0031-9155/59/3/679] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
In SPECT, the collimator is a crucial element in controlling image quality. We take a task performance approach to collimator performance evaluation in which an ideal observer is applied to the raw camera data without regard to the subsequent reconstruction stage. The clinical context of our collimator study is one of searching for and detecting neuroendocrine tumor metastases in the liver as seen in In-111 Octreotide SPECT. Our task involves detection and localization of a signal and thus differs from the conventionally used detection-only task. The scalar task performance metric is ALROC, the area under the localization receiver operating characteristic curve. Since In-111 emits photons at both 171 and 245 keV, the higher energy emissions can contribute significant septal scatter and penetration. Our collimator evaluations address a question previously considered by Mähler et al (2012 IEEE Trans. Nucl. Sci. 59 47–53) who used a different methodology: does allowing a limited amount of septal scatter and penetration yield improved task performance? We used simulation methods to evaluate five parallel-hole collimators. The collimators had roughly equal geometric sensitivity and resolution but a range of contributions from septal effects leading to variations in total sensitivity and resolution. We found that the best performance was obtained with a collimator that allowed a moderate amount of septal scatter and penetration.
Collapse
|
28
|
FIORI MARCELO, MUSÉ PABLO, SAPIRO GUILLERMO. A COMPLETE SYSTEM FOR CANDIDATE POLYPS DETECTION IN VIRTUAL COLONOSCOPY. INT J PATTERN RECOGN 2014. [DOI: 10.1142/s0218001414600143] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
We present a computer-aided detection pipeline for polyp detection in Computer tomographic colonography. The first stage of the pipeline consists of a simple colon segmentation technique that enhances polyps, which is followed by an adaptive-scale candidate polyp delineation, in order to capture the appropriate polyp size. In the last step, candidates are classified based on new texture and geometric features that consider both the information in the candidate polyp location and its immediate surrounding area. The system is tested with ground truth data, including flat and small polyps which are hard to detect even with optical colonoscopy. We achieve 100% sensitivity for polyps larger than 6 mm in size with just 0.9 false positives per case, and 93% sensitivity with 2.8 false positives per case for polyps larger than 3 mm in size.
Collapse
Affiliation(s)
- MARCELO FIORI
- Facultad de Ingeniería, Universidad de la República, Uruguay
| | - PABLO MUSÉ
- Facultad de Ingeniería, Universidad de la República, Uruguay
| | | |
Collapse
|
29
|
Firmino M, Morais AH, Mendoça RM, Dantas MR, Hekis HR, Valentim R. Computer-aided detection system for lung cancer in computed tomography scans: review and future prospects. Biomed Eng Online 2014; 13:41. [PMID: 24713067 PMCID: PMC3995505 DOI: 10.1186/1475-925x-13-41] [Citation(s) in RCA: 92] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2014] [Accepted: 03/28/2014] [Indexed: 12/25/2022] Open
Abstract
Introduction The goal of this paper is to present a critical review of major Computer-Aided Detection systems (CADe) for lung cancer in order to identify challenges for future research. CADe systems must meet the following requirements: improve the performance of radiologists providing high sensitivity in the diagnosis, a low number of false positives (FP), have high processing speed, present high level of automation, low cost (of implementation, training, support and maintenance), the ability to detect different types and shapes of nodules, and software security assurance. Methods The relevant literature related to “CADe for lung cancer” was obtained from PubMed, IEEEXplore and Science Direct database. Articles published from 2009 to 2013, and some articles previously published, were used. A systemic analysis was made on these articles and the results were summarized. Discussion Based on literature search, it was observed that many if not all systems described in this survey have the potential to be important in clinical practice. However, no significant improvement was observed in sensitivity, number of false positives, level of automation and ability to detect different types and shapes of nodules in the studied period. Challenges were presented for future research. Conclusions Further research is needed to improve existing systems and propose new solutions. For this, we believe that collaborative efforts through the creation of open source software communities are necessary to develop a CADe system with all the requirements mentioned and with a short development cycle. In addition, future CADe systems should improve the level of automation, through integration with picture archiving and communication systems (PACS) and the electronic record of the patient, decrease the number of false positives, measure the evolution of tumors, evaluate the evolution of the oncological treatment, and its possible prognosis.
Collapse
Affiliation(s)
- Macedo Firmino
- Department of Information and Computer Science, Federal Institute of Rio Grande do Norte (IFRN), Natal, Brazil.
| | | | | | | | | | | |
Collapse
|
30
|
Petrick N, Sahiner B, Armato SG, Bert A, Correale L, Delsanto S, Freedman MT, Fryd D, Gur D, Hadjiiski L, Huo Z, Jiang Y, Morra L, Paquerault S, Raykar V, Samuelson F, Summers RM, Tourassi G, Yoshida H, Zheng B, Zhou C, Chan HP. Evaluation of computer-aided detection and diagnosis systems. Med Phys 2014; 40:087001. [PMID: 23927365 DOI: 10.1118/1.4816310] [Citation(s) in RCA: 65] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Computer-aided detection and diagnosis (CAD) systems are increasingly being used as an aid by clinicians for detection and interpretation of diseases. Computer-aided detection systems mark regions of an image that may reveal specific abnormalities and are used to alert clinicians to these regions during image interpretation. Computer-aided diagnosis systems provide an assessment of a disease using image-based information alone or in combination with other relevant diagnostic data and are used by clinicians as a decision support in developing their diagnoses. While CAD systems are commercially available, standardized approaches for evaluating and reporting their performance have not yet been fully formalized in the literature or in a standardization effort. This deficiency has led to difficulty in the comparison of CAD devices and in understanding how the reported performance might translate into clinical practice. To address these important issues, the American Association of Physicists in Medicine (AAPM) formed the Computer Aided Detection in Diagnostic Imaging Subcommittee (CADSC), in part, to develop recommendations on approaches for assessing CAD system performance. The purpose of this paper is to convey the opinions of the AAPM CADSC members and to stimulate the development of consensus approaches and "best practices" for evaluating CAD systems. Both the assessment of a standalone CAD system and the evaluation of the impact of CAD on end-users are discussed. It is hoped that awareness of these important evaluation elements and the CADSC recommendations will lead to further development of structured guidelines for CAD performance assessment. Proper assessment of CAD system performance is expected to increase the understanding of a CAD system's effectiveness and limitations, which is expected to stimulate further research and development efforts on CAD technologies, reduce problems due to improper use, and eventually improve the utility and efficacy of CAD in clinical practice.
Collapse
Affiliation(s)
- Nicholas Petrick
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, Maryland 20993, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
31
|
Feasibility of using the marginal blood vessels as reference landmarks for CT colonography. AJR Am J Roentgenol 2014; 202:W50-8. [PMID: 24370165 DOI: 10.2214/ajr.12.10463] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
OBJECTIVE The purpose of this study was to show the spatial relationship of the colonic marginal blood vessels and the teniae coli on CT colonography (CTC) and the use of the marginal blood vessels for supine-prone registration of polyps and for determination of proper connectivity of collapsed colonic segments. MATERIALS AND METHODS We manually labeled the marginal blood vessels on 15 CTC examinations. Colon segmentation, centerline extraction, teniae detection, and teniae identification were automatically performed. For assessment of their spatial relationships, the distances from the marginal blood vessels to the three teniae coli and to the colon were measured. Student t tests (paired, two-tailed) were performed to evaluate the differences among these distances. To evaluate the reliability of the marginal vessels as reference points for polyp correlation, we analyzed 20 polyps from 20 additional patients who underwent supine and prone CTC. The average difference of the circumferential polyp position on the supine and prone scans was computed. Student t tests (paired, two-tailed) were performed to evaluate the supine-prone differences of the distance. We performed a study on 10 CTC studies from 10 patients with collapsed colonic segments by manually tracing the marginal blood vessels near the collapsed regions to resolve the ambiguity of the colon path. RESULTS The average distances (± SD) from the marginal blood vessels to the tenia mesocolica, tenia omentalis, and tenia libera were 20.1 ± 3.1 mm (95% CI, 18.5-21.6 mm), 39.5 ± 4.8 mm (37.1-42.0 mm), and 36.9 ± 4.2 mm (34.8-39.1 mm), respectively. Pairwise comparison showed that these distances to the tenia libera and tenia omentalis were significantly different from the distance to the tenia mesocolica (p < 0.001). The average distance from the marginal blood vessels to the colon wall was 15.3 ± 2.0 mm (14.2-16.3 mm). For polyp localization, the average difference of the circumferential polyp position on the supine and prone scans was 9.6 ± 9.4 mm (5.5-13.7 mm) (p = 0.15) and expressed as a percentage of the colon circumference was 3.1% ± 2.0% (2.3-4.0%) (p = 0.83). We were able to trace the marginal blood vessels for 10 collapsed colonic segments and determine the paths of the colon in these regions. CONCLUSION The marginal blood vessels run parallel to the colon in proximity to the tenia mesocolica and enable accurate supine-prone registration of polyps and localization of the colon path in areas of collapse. Thus, the marginal blood vessels may be used as reference landmarks complementary to the colon centerline and teniae coli.
Collapse
|
32
|
Lu L, Devarakota P, Vikal S, Wu D, Zheng Y, Wolf M. Computer Aided Diagnosis Using Multilevel Image Features on Large-Scale Evaluation. MEDICAL COMPUTER VISION. LARGE DATA IN MEDICAL IMAGING 2014. [DOI: 10.1007/978-3-319-05530-5_16] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
|
33
|
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.
Collapse
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).
| | | |
Collapse
|
34
|
Teramoto A, Fujita H, Takahashi K, Yamamuro O, Tamaki T, Nishio M, Kobayashi T. Hybrid method for the detection of pulmonary nodules using positron emission tomography/computed tomography: a preliminary study. Int J Comput Assist Radiol Surg 2013; 9:59-69. [PMID: 23793722 DOI: 10.1007/s11548-013-0910-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2013] [Accepted: 06/06/2013] [Indexed: 11/25/2022]
Abstract
PURPOSE In this study, an automated scheme for detecting pulmonary nodules using a novel hybrid PET/CT approach is proposed, which is designed to detect pulmonary nodules by combining data from both sets of images. METHODS Solitary nodules were detected on CT by a cylindrical filter that we developed previously, and in the PET imaging, high-uptake regions were detected automatically using thresholding based on standardized uptake values along with false-positive reduction by means of the anatomical information obtained from the CT images. Initial candidate nodules were identified by combining the results. False positives among the initial candidates were eliminated by a rule-based classifier and three support vector machines on the basis of the characteristic features obtained from CT and PET images. RESULTS We validated the proposed method using 100 cases of PET/CT images that were obtained during a cancer-screening program. The detection performance was assessed by free-response receiver operating characteristic (FROC) analysis. The sensitivity was 83.0% with the number of false positives/case at 5.0, and it was 8% higher than the sensitivity of independent detection systems using CT or PET images alone. CONCLUSION Detection performance indicates that our method may be of practical use for the identification of pulmonary nodules in PET/CT images.
Collapse
Affiliation(s)
- Atsushi Teramoto
- Faculty of Radiological Technology, School of Health Sciences, Fujita Health University, 1-98 Dengakugakubo, Kutsukake, Toyoake, Aichi, 470-1192, Japan,
| | | | | | | | | | | | | |
Collapse
|
35
|
Detection of pulmonary nodules in CT images based on fuzzy integrated active contour model and hybrid parametric mixture model. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2013; 2013:515386. [PMID: 23690876 PMCID: PMC3652289 DOI: 10.1155/2013/515386] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2013] [Revised: 03/12/2013] [Accepted: 03/23/2013] [Indexed: 11/27/2022]
Abstract
The segmentation and detection of various types of nodules in a Computer-aided detection
(CAD) system present various challenges, especially when (1) the nodule is connected to a vessel
and they have very similar intensities; (2) the nodule with ground-glass opacity (GGO)
characteristic possesses typical weak edges and intensity inhomogeneity, and hence it is difficult
to define the boundaries. Traditional segmentation methods may cause problems of boundary
leakage and “weak” local minima. This paper deals with the above mentioned problems. An
improved detection method which combines a fuzzy integrated active contour model
(FIACM)-based segmentation method, a segmentation refinement method based on Parametric
Mixture Model (PMM) of juxta-vascular nodules, and a knowledge-based C-SVM
(Cost-sensitive Support Vector Machines) classifier, is proposed for detecting various types of
pulmonary nodules in computerized tomography (CT) images. Our approach has several novel
aspects: (1) In the proposed FIACM model, edge and local region information is incorporated.
The fuzzy energy is used as the motivation power for the evolution of the active contour. (2) A
hybrid PMM Model of juxta-vascular nodules combining appearance and geometric
information is constructed for segmentation refinement of juxta-vascular nodules. Experimental
results of detection for pulmonary nodules show desirable performances of the proposed
method.
Collapse
|
36
|
Suzuki K. Machine Learning in Computer-aided Diagnosis of the Thorax and Colon in CT: A Survey. IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2013; E96-D:772-783. [PMID: 24174708 PMCID: PMC3810349 DOI: 10.1587/transinf.e96.d.772] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Computer-aided detection (CADe) and diagnosis (CAD) has been a rapidly growing, active area of research in medical imaging. Machine leaning (ML) plays an essential role in CAD, because objects such as lesions and organs may not be represented accurately by a simple equation; thus, medical pattern recognition essentially require "learning from examples." One of the most popular uses of ML is the classification of objects such as lesion candidates into certain classes (e.g., abnormal or normal, and lesions or non-lesions) based on input features (e.g., contrast and area) obtained from segmented lesion candidates. The task of ML is to determine "optimal" boundaries for separating classes in the multidimensional feature space which is formed by the input features. ML algorithms for classification include linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), multilayer perceptrons, and support vector machines (SVM). Recently, pixel/voxel-based ML (PML) emerged in medical image processing/analysis, which uses pixel/voxel values in images directly, instead of features calculated from segmented lesions, as input information; thus, feature calculation or segmentation is not required. In this paper, ML techniques used in CAD schemes for detection and diagnosis of lung nodules in thoracic CT and for detection of polyps in CT colonography (CTC) are surveyed and reviewed.
Collapse
Affiliation(s)
- Kenji Suzuki
- Department of Radiology, The University of Chicago, Chicago, IL 60637, USA
| |
Collapse
|
37
|
Automatic Detection of 2D and 3D Lung Nodules in Chest Spiral CT Scans. Int J Biomed Imaging 2013; 2013:517632. [PMID: 23509444 PMCID: PMC3590446 DOI: 10.1155/2013/517632] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2012] [Revised: 12/13/2012] [Accepted: 12/14/2012] [Indexed: 12/05/2022] Open
Abstract
Automatic detection of lung nodules is an important problem in computer analysis of chest radiographs. In this paper, we propose a novel algorithm for isolating lung abnormalities (nodules) from spiral chest low-dose CT (LDCT) scans. The proposed algorithm consists of three main steps. The first step isolates the lung nodules, arteries, veins, bronchi, and bronchioles from the surrounding anatomical structures. The second step detects lung nodules using deformable 3D and 2D templates describing typical geometry and gray-level distribution within the nodules of the same type. The detection combines the normalized cross-correlation template matching and a genetic optimization algorithm. The final step eliminates the false positive nodules (FPNs) using three features that robustly define the true lung nodules. Experiments with 200 CT data sets show that the proposed approach provided comparable results with respect to the experts.
Collapse
|
38
|
Automated Pulmonary Nodule Detection System in Computed Tomography Images: A Hierarchical Block Classification Approach. ENTROPY 2013. [DOI: 10.3390/e15020507] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
39
|
Computer-aided diagnosis systems for lung cancer: challenges and methodologies. Int J Biomed Imaging 2013; 2013:942353. [PMID: 23431282 PMCID: PMC3570946 DOI: 10.1155/2013/942353] [Citation(s) in RCA: 116] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2012] [Accepted: 11/20/2012] [Indexed: 11/24/2022] Open
Abstract
This paper overviews one of the most important, interesting, and challenging problems in oncology, the problem of lung cancer diagnosis. Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient's chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant. This paper overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps. For each technique, various aspects of technical issues, implemented methodologies, training and testing databases, and validation methods, as well as achieved performances, are described. In addition, the paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems.
Collapse
|
40
|
Chen SC, Yang ST, Yu YH, Chen CY, Shen WC. Pleura detection in chest computed tomography with application for nodule detection. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2013:2344-2347. [PMID: 24110195 DOI: 10.1109/embc.2013.6610008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
This paper proposes an efficient algorithm for detecting pleural objects that come in contact with a nodule. To reduce complexity, the algorithm recursively performed a curve-fitting method on each slice of the volume of interest to locate the object between the parietal and visceral pleurae surfaces and measured the quality of the fitting curve. When a nodule contacted the surfaces of the chest wall or diaphragm, they were automatically separated using the fitting curve. The algorithm was performed on 864 slices of 40 nodules. The segmentation results were visually inspected by a consensus of attending physicians to search for any segmentation errors. The consensus accepted 93.6% of the segmentation results.
Collapse
|
41
|
Suzuki K. A review of computer-aided diagnosis in thoracic and colonic imaging. Quant Imaging Med Surg 2012; 2:163-76. [PMID: 23256078 DOI: 10.3978/j.issn.2223-4292.2012.09.02] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2012] [Accepted: 09/19/2012] [Indexed: 12/24/2022]
Abstract
Medical imaging has been indispensable in medicine since the discovery of x-rays. Medical imaging offers useful information on patients' medical conditions and on the causes of their symptoms and diseases. As imaging technologies advance, a large number of medical images are produced which physicians/radiologists must interpret. Thus, computer aids are demanded and become indispensable in physicians' decision making based on medical images. Consequently, computer-aided detection and diagnosis (CAD) has been investigated and has been an active research area in medical imaging. CAD is defined as detection and/or diagnosis made by a radiologist/physician who takes into account the computer output as a "second opinion". In CAD research, detection and diagnosis of lung and colorectal cancer in thoracic and colonic imaging constitute major areas, because lung and colorectal cancers are the leading and second leading causes, respectively, of cancer deaths in the U.S. and also in other countries. In this review, CAD of the thorax and colon, including CAD for detection and diagnosis of lung nodules in thoracic CT, and that for detection of polyps in CT colonography, are reviewed.
Collapse
Affiliation(s)
- Kenji Suzuki
- Department of Radiology, The University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA
| |
Collapse
|
42
|
Genetic programming-based feature transform and classification for the automatic detection of pulmonary nodules on computed tomography images. Inf Sci (N Y) 2012. [DOI: 10.1016/j.ins.2012.05.008] [Citation(s) in RCA: 86] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
43
|
Guo W, Li Q. High performance lung nodule detection schemes in CT using local and global information. Med Phys 2012; 39:5157-68. [PMID: 22894441 DOI: 10.1118/1.4737109] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
PURPOSE A key issue in current computer-aided diagnostic (CAD) schemes for nodule detection in CT is the large number of false positives, because current schemes use only global three-dimensional (3D) information to detect nodules and discard useful local two-dimensional (2D) information. Thus, the authors integrated local and global information to markedly improve the performance levels of CAD schemes. METHODS Our database was obtained from the standard CT lung nodule database created by the Lung Image Database Consortium (LIDC). It consisted of 85 CT scans with 111 nodules of 3 mm or larger in diameter. The 111 nodules were confirmed by at least two of the four radiologists participated in the LIDC. Twenty-six nodules were missed by two of the four radiologists and were thus very difficult to detect. The authors developed five CAD schemes for nodule detection in CT using global 3D information (3D scheme), local 2D information (2D scheme), and both local and global information (2D + 3D scheme, 2D - 3D scheme, and 3D - 2D scheme). The 3D scheme, which was developed previously, used only global 3D information and discarded local 2D information, as other CAD schemes did. The 2D scheme used a uniform viewpoint reformation technique to decompose a 3D nodule candidate into a set of 2D reformatted images generated from representative viewpoints, and selected and used "effective" 2D reformatted images to remove false positives. The 2D + 3D scheme, 2D - 3D scheme, and 3D - 2D scheme used complementary local and global information in different ways to further improve the performance of lung nodule detection. The authors employed a leave-one-scan-out testing method for evaluation of the performance levels of the five CAD schemes. RESULTS At the sensitivities of 85%, 80%, and 75%, the existing 3D scheme reported 17.3, 7.4, and 2.8 false positives per scan, respectively; the 2D scheme improved the detection performance and reduced the numbers of false positives to 7.6, 2.5, and 1.3 per scan; the 2D + 3D scheme further reduced those to 2.7, 1.9, and 0.6 per scan; the 2D - 3D scheme reduced those to 7.6, 2.1, and 0.8 per scan; and the 3D - 2D scheme reduced those to 17.3, 1.6, and 1.0 per scan. CONCLUSIONS The local 2D information appears to be more useful than the global 3D information for nodule detection, particularly, when it is integrated with 3D information.
Collapse
Affiliation(s)
- Wei Guo
- School of Computer, Shenyang Aerospace University, Daoyi Development District, Shenyang, Liaoning 110136, China
| | | |
Collapse
|
44
|
Wang S, Summers RM. Machine learning and radiology. Med Image Anal 2012; 16:933-51. [PMID: 22465077 PMCID: PMC3372692 DOI: 10.1016/j.media.2012.02.005] [Citation(s) in RCA: 322] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2011] [Revised: 01/05/2012] [Accepted: 02/12/2012] [Indexed: 02/06/2023]
Abstract
In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers.
Collapse
Affiliation(s)
- Shijun Wang
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Building 10 Room 1C224D MSC 1182, Bethesda, MD 20892-1182
| | - Ronald M. Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Building 10 Room 1C224D MSC 1182, Bethesda, MD 20892-1182
| |
Collapse
|
45
|
Wei Z, Yao J, Wang S, Liu J, Summers RM. Automated teniae coli detection and identification on computed tomographic colonography. Med Phys 2012; 39:964-75. [PMID: 22320805 DOI: 10.1118/1.3679013] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Computed tomographic colonography (CTC) is a minimally invasive technique for colonic polyps and cancer screening. Teniae coli are three bands of longitudinal smooth muscle on the colon surface. Teniae coli are important anatomically meaningful landmarks on human colon. In this paper, the authors propose an automatic teniae coli detection method for CT colonography. METHODS The original CTC slices are first segmented and reconstructed to a 3D colon surface. Then, the 3D colon surface is unfolded using a reversible projection technique. After that the unfolded colon is projected to a 2D height map. The teniae coli are detected using the height map and then reversely projected back to the 3D colon. Since teniae are located at the junctions where the haustral folds meet, the authors apply 2D Gabor filter banks to extract features of haustral folds. The maximum response of the filter banks is then selected as the feature image. The fold centers are then identified based on local maxima and thresholding on the feature image. Connecting the fold centers yields a path of the folds. Teniae coli are extracted as lines running between the fold paths. The authors used the spatial relationship between ileocecal valve (ICV) and teniae mesocolica (TM) to identify the TM, then the teniae omentalis (TO) and the teniae libera (TL) can be identified subsequently. RESULTS The authors tested the proposed method on 47 cases of 37 patients, 10 of the patients with both supine and prone CT scans. The proposed method yielded performance with an average normalized root mean square error (RMSE) ( ± standard deviation [95% confidence interval]) of 4.87% ( ± 2.93%, [4.05% 5.69%]). CONCLUSIONS The proposed fully-automated teniae coli detection and identification method is accurate and promising for future clinical applications.
Collapse
Affiliation(s)
- Zhuoshi Wei
- National Institutes of Health Clinical Center, Bethesda, MD 20892-1182, USA
| | | | | | | | | |
Collapse
|
46
|
Wang S, McKenna MT, Nguyen TB, Burns JE, Petrick N, Sahiner B, Summers RM. Seeing is believing: video classification for computed tomographic colonography using multiple-instance learning. IEEE TRANSACTIONS ON MEDICAL IMAGING 2012; 31:1141-53. [PMID: 22552333 PMCID: PMC3480731 DOI: 10.1109/tmi.2012.2187304] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
In this paper, we present development and testing results for a novel colonic polyp classification method for use as part of a computed tomographic colonography (CTC) computer-aided detection (CAD) system. Inspired by the interpretative methodology of radiologists using 3-D fly-through mode in CTC reading, we have developed an algorithm which utilizes sequences of images (referred to here as videos) for classification of CAD marks. For each CAD mark, we created a video composed of a series of intraluminal, volume-rendered images visualizing the detection from multiple viewpoints. We then framed the video classification question as a multiple-instance learning (MIL) problem. Since a positive (negative) bag may contain negative (positive) instances, which in our case depends on the viewing angles and camera distance to the target, we developed a novel MIL paradigm to accommodate this class of problems. We solved the new MIL problem by maximizing a L2-norm soft margin using semidefinite programming, which can optimize relevant parameters automatically. We tested our method by analyzing a CTC data set obtained from 50 patients from three medical centers. Our proposed method showed significantly better performance compared with several traditional MIL methods.
Collapse
Affiliation(s)
- Shijun Wang
- National Institutes of Health, Bethesda, MD, 20892 USA
| | | | - Tan B. Nguyen
- National Institutes of Health, Bethesda, MD, 20892 USA
| | - Joseph E. Burns
- Department of Radiological Sciences, University of California, Irvine, School of Medicine, Orange, CA 92868 USA
| | - Nicholas Petrick
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD 20993 USA
| | - Berkman Sahiner
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD 20993 USA
| | | |
Collapse
|
47
|
Linguraru MG, Panjwani N, Fletcher JG, Summers RM. Automated image-based colon cleansing for laxative-free CT colonography computer-aided polyp detection. Med Phys 2012; 38:6633-42. [PMID: 22149845 DOI: 10.1118/1.3662918] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
PURPOSE To evaluate the performance of a computer-aided detection (CAD) system for detecting colonic polyps at noncathartic computed tomography colonography (CTC) in conjunction with an automated image-based colon cleansing algorithm. METHODS An automated colon cleansing algorithm was designed to detect and subtract tagged-stool, accounting for heterogeneity and poor tagging, to be used in conjunction with a colon CAD system. The method is locally adaptive and combines intensity, shape, and texture analysis with probabilistic optimization. CTC data from cathartic-free bowel preparation were acquired for testing and training the parameters. Patients underwent various colonic preparations with barium or Gastroview in divided doses over 48 h before scanning. No laxatives were administered and no dietary modifications were required. Cases were selected from a polyp-enriched cohort and included scans in which at least 90% of the solid stool was visually estimated to be tagged and each colonic segment was distended in either the prone or supine view. The CAD system was run comparatively with and without the stool subtraction algorithm. RESULTS The dataset comprised 38 CTC scans from prone and/or supine scans of 19 patients containing 44 polyps larger than 10 mm (22 unique polyps, if matched between prone and supine scans). The results are robust on fine details around folds, thin-stool linings on the colonic wall, near polyps and in large fluid/stool pools. The sensitivity of the CAD system is 70.5% per polyp at a rate of 5.75 false positives/scan without using the stool subtraction module. This detection improved significantly (p = 0.009) after automated colon cleansing on cathartic-free data to 86.4% true positive rate at 5.75 false positives/scan. CONCLUSIONS An automated image-based colon cleansing algorithm designed to overcome the challenges of the noncathartic colon significantly improves the sensitivity of colon CAD by approximately 15%.
Collapse
Affiliation(s)
- Marius George Linguraru
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Drive, Bethesda, Maryland 20892, USA.
| | | | | | | |
Collapse
|
48
|
Li B, Zhang J, Tian L, Tan L, Xiang S, Ou S. Intelligent Recognition of Lung Nodule Combining Rule-based and C-SVM Classifiers. INT J COMPUT INT SYS 2012. [DOI: 10.1080/18756891.2012.670523] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
|
49
|
Miranda AA, Caelen O, Bontempi G. Machine Learning for Automated Polyp Detection in Computed Tomography Colonography. Mach Learn 2012. [DOI: 10.4018/978-1-60960-818-7.ch407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This chapter presents a comprehensive scheme for automated detection of colorectal polyps in computed tomography colonography (CTC) with particular emphasis on robust learning algorithms that differentiate polyps from non-polyp shapes. The authors’ automated CTC scheme introduces two orientation independent features which encode the shape characteristics that aid in classification of polyps and non-polyps with high accuracy, low false positive rate, and low computations making the scheme suitable for colorectal cancer screening initiatives. Experiments using state-of-the-art machine learning algorithms viz., lazy learning, support vector machines, and naïve Bayes classifiers reveal the robustness of the two features in detecting polyps at 100% sensitivity for polyps with diameter greater than 10 mm while attaining total low false positive rates, respectively, of 3.05, 3.47 and 0.71 per CTC dataset at specificities above 99% when tested on 58 CTC datasets. The results were validated using colonoscopy reports provided by expert radiologists.
Collapse
|
50
|
Tan M, Deklerck R, Jansen B, Bister M, Cornelis J. A novel computer-aided lung nodule detection system for CT images. Med Phys 2011; 38:5630-45. [PMID: 21992380 DOI: 10.1118/1.3633941] [Citation(s) in RCA: 158] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE The paper presents a complete computer-aided detection (CAD) system for the detection of lung nodules in computed tomography images. A new mixed feature selection and classification methodology is applied for the first time on a difficult medical image analysis problem. METHODS The CAD system was trained and tested on images from the publicly available Lung Image Database Consortium (LIDC) on the National Cancer Institute website. The detection stage of the system consists of a nodule segmentation method based on nodule and vessel enhancement filters and a computed divergence feature to locate the centers of the nodule clusters. In the subsequent classification stage, invariant features, defined on a gauge coordinates system, are used to differentiate between real nodules and some forms of blood vessels that are easily generating false positive detections. The performance of the novel feature-selective classifier based on genetic algorithms and artificial neural networks (ANNs) is compared with that of two other established classifiers, namely, support vector machines (SVMs) and fixed-topology neural networks. A set of 235 randomly selected cases from the LIDC database was used to train the CAD system. The system has been tested on 125 independent cases from the LIDC database. RESULTS The overall performance of the fixed-topology ANN classifier slightly exceeds that of the other classifiers, provided the number of internal ANN nodes is chosen well. Making educated guesses about the number of internal ANN nodes is not needed in the new feature-selective classifier, and therefore this classifier remains interesting due to its flexibility and adaptability to the complexity of the classification problem to be solved. Our fixed-topology ANN classifier with 11 hidden nodes reaches a detection sensitivity of 87.5% with an average of four false positives per scan, for nodules with diameter greater than or equal to 3 mm. Analysis of the false positive items reveals that a considerable proportion (18%) of them are smaller nodules, less than 3 mm in diameter. CONCLUSIONS A complete CAD system incorporating novel features is presented, and its performance with three separate classifiers is compared and analyzed. The overall performance of our CAD system equipped with any of the three classifiers is well with respect to other methods described in literature.
Collapse
Affiliation(s)
- Maxine Tan
- Department of Electronics and Informatics , Vrije Universiteit Brussel, Brussel, Belgium
| | | | | | | | | |
Collapse
|