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Gennari AG, Rossi A, De Cecco CN, van Assen M, Sartoretti T, Giannopoulos AA, Schwyzer M, Huellner MW, Messerli M. Artificial intelligence in coronary artery calcium score: rationale, different approaches, and outcomes. Int J Cardiovasc Imaging 2024; 40:951-966. [PMID: 38700819 PMCID: PMC11147943 DOI: 10.1007/s10554-024-03080-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Accepted: 03/09/2024] [Indexed: 06/05/2024]
Abstract
Almost 35 years after its introduction, coronary artery calcium score (CACS) not only survived technological advances but became one of the cornerstones of contemporary cardiovascular imaging. Its simplicity and quantitative nature established it as one of the most robust approaches for atherosclerotic cardiovascular disease risk stratification in primary prevention and a powerful tool to guide therapeutic choices. Groundbreaking advances in computational models and computer power translated into a surge of artificial intelligence (AI)-based approaches directly or indirectly linked to CACS analysis. This review aims to provide essential knowledge on the AI-based techniques currently applied to CACS, setting the stage for a holistic analysis of the use of these techniques in coronary artery calcium imaging. While the focus of the review will be detailing the evidence, strengths, and limitations of end-to-end CACS algorithms in electrocardiography-gated and non-gated scans, the current role of deep-learning image reconstructions, segmentation techniques, and combined applications such as simultaneous coronary artery calcium and pulmonary nodule segmentation, will also be discussed.
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Affiliation(s)
- Antonio G Gennari
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Alexia Rossi
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Carlo N De Cecco
- Division of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Emory University, Atlanta, GA, USA
| | - Marly van Assen
- Translational Laboratory for Cardiothoracic Imaging and Artificial Intelligence, Emory University, Atlanta, GA, USA
| | - Thomas Sartoretti
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Andreas A Giannopoulos
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland
| | - Moritz Schwyzer
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Martin W Huellner
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Michael Messerli
- Department of Nuclear Medicine, University Hospital Zurich, Rämistrasse 100, Zurich, 8091, Switzerland.
- University of Zurich, Zurich, Switzerland.
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Homma S, Kato K. Validity of Atherosclerotic Calcified Lesions Observed on Low-Dose Computed Tomography and Cardio-Ankle Vascular Index as Surrogate Markers of Atherosclerosis Progression. Angiology 2024; 75:349-358. [PMID: 36787785 DOI: 10.1177/00033197231155963] [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] [Indexed: 02/16/2023]
Abstract
The significance of atherosclerotic calcified lesions observed on low-dose computed tomography (LDCT) performed during general checkups was investigated. The coronary arteries (CA), ascending aorta and aortic arch (AAAA), descending thoracic aorta (DTA), and abdominal aorta (AA) were examined. Semiquantitative calcified index analysis of the DTA and AA in terms of atherosclerosis risk factors and cardio-ankle vascular index (CAVI) measurements was also performed. We included 1594 participants (mean age: 59.2 years; range: 31-91 years). The prevalence of calcified lesions was 71.0%, 66.6%, 57.2%, and 37.9% in the AA, CA, AAAA, and DTA, respectively. Age-related advances in calcification among participants with no major risk factors, revealed that calcification appeared earliest in the AA, followed by the CA, AAAA, and DTA. Participants with calcified lesions in all arteries had a significantly greater CAVI than those without calcification. The CAVI was negatively correlated with low-density lipoprotein cholesterol levels, particularly in participants without calcified lesions in the DTA. Calcified lesions on LDCT could indicate the end stage of atherosclerotic lesions. The CAVI can be used to assess atherosclerotic changes at all stages of disease progression. A combination of LDCT and CAVI could be used as a routine non-invasive assessment of atherosclerosis.
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Affiliation(s)
- Satoki Homma
- Health Care Center in Saitama Medical Center of the Japan Community Health Care Organization, Saitama, Japan
- Faculty of Nursing and Medical Care, Keio University & Keio Research Institute at SFC (Shonan Fujisawa Campus), Fujisawa, Japan
| | - Kiyoe Kato
- Center of General Health Check-Up, Saiseikai Central Hospital, Tokyo, Japan
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Abdelrahman K, Shiyovich A, Huck DM, Berman AN, Weber B, Gupta S, Cardoso R, Blankstein R. Artificial Intelligence in Coronary Artery Calcium Scoring Detection and Quantification. Diagnostics (Basel) 2024; 14:125. [PMID: 38248002 PMCID: PMC10814920 DOI: 10.3390/diagnostics14020125] [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: 11/08/2023] [Revised: 12/25/2023] [Accepted: 12/27/2023] [Indexed: 01/23/2024] Open
Abstract
Coronary artery calcium (CAC) is a marker of coronary atherosclerosis, and the presence and severity of CAC have been shown to be powerful predictors of future cardiovascular events. Due to its value in risk discrimination and reclassification beyond traditional risk factors, CAC has been supported by recent guidelines, particularly for the purposes of informing shared decision-making regarding the use of preventive therapies. In addition to dedicated ECG-gated CAC scans, the presence and severity of CAC can also be accurately estimated on non-contrast chest computed tomography scans performed for other clinical indications. However, the presence of such "incidental" CAC is rarely reported. Advances in artificial intelligence have now enabled automatic CAC scoring for both cardiac and non-cardiac CT scans. Various AI approaches, from rule-based models to machine learning algorithms and deep learning, have been applied to automate CAC scoring. Convolutional neural networks, a deep learning technique, have had the most successful approach, with high agreement with manual scoring demonstrated in multiple studies. Such automated CAC measurements may enable wider and more accurate detection of CAC from non-gated CT studies, thus improving the efficiency of healthcare systems to identify and treat previously undiagnosed coronary artery disease.
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Affiliation(s)
| | | | | | | | | | | | | | - Ron Blankstein
- Departments of Medicine (Cardiovascular Division) and Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
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Gautam A, Raghav P, Subramaniam V, Kumar S, Kumar S, Jain D, Verma A, Singh P, Singhal M, Gupta V, Rathore S, Iyengar S, Rathore S. Fully Automated Agatston Score Calculation From Electrocardiography-Gated Cardiac Computed Tomography Using Deep Learning and Multi-Organ Segmentation: A Validation Study. Angiology 2024:33197231225286. [PMID: 38166442 DOI: 10.1177/00033197231225286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2024]
Abstract
To evaluate deep learning-based calcium segmentation and quantification on ECG-gated cardiac CT scans compared with manual evaluation. Automated calcium quantification was performed using a neural network based on mask regions with convolutional neural networks (R-CNNs) for multi-organ segmentation. Manual evaluation of calcium was carried out using proprietary software. This is a retrospective study of archived data. This study used 40 patients to train the segmentation model and 110 patients were used for the validation of the algorithm. The Pearson correlation coefficient between the reference actual and the computed predictive scores shows high level of correlation (0.84; P < .001) and high limits of agreement (±1.96 SD; -2000, 2000) in Bland-Altman plot analysis. The proposed method correctly classifies the risk group in 75.2% and classifies the subjects in the same group. In total, 81% of the predictive scores lie in the same categories and only seven patients out of 110 were more than one category off. For the presence/absence of coronary artery calcifications, the deep learning model achieved a sensitivity of 90% and a specificity of 94%. Fully automated model shows good correlation compared with reference standards. Automating process reduces evaluation time and optimizes clinical calcium scoring without additional resources.
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Affiliation(s)
| | | | | | - Sunil Kumar
- Department of Radiology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, India
| | - Sudeep Kumar
- Department of Cardiology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, India
| | - Dharmendra Jain
- Department of Cardiology, Banaras Hindu University, Varanasi, India
| | - Ashish Verma
- Department of Radiology, Banaras Hindu University, Varanasi, India
| | - Parminder Singh
- Department of Cardiology, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Manphoul Singhal
- Department of Radiology, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Vikash Gupta
- Department of Radiology, Mayo Clinic, Jacksonville, FL, USA
| | | | - Srikanth Iyengar
- Department of Radiology, Frimley Park Hospital NHS Foundation Trust, Camberley, UK
| | - Sudhir Rathore
- Department of Cardiology, Frimley Park Hospital NHS Foundation Trust, Camberley, UK
- University of Surrey, Guildford, UK
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Miller RJH, Pieszko K, Shanbhag A, Feher A, Lemley M, Killekar A, Kavanagh PB, Van Kriekinge SD, Liang JX, Huang C, Miller EJ, Bateman T, Berman DS, Dey D, Slomka PJ. Deep Learning Coronary Artery Calcium Scores from SPECT/CT Attenuation Maps Improve Prediction of Major Adverse Cardiac Events. J Nucl Med 2023; 64:652-658. [PMID: 36207138 PMCID: PMC10071789 DOI: 10.2967/jnumed.122.264423] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 10/04/2022] [Accepted: 10/04/2022] [Indexed: 11/16/2022] Open
Abstract
Low-dose ungated CT attenuation correction (CTAC) scans are commonly obtained with SPECT/CT myocardial perfusion imaging. Despite the characteristically low image quality of CTAC, deep learning (DL) can potentially quantify coronary artery calcium (CAC) from these scans in an automatic manner. We evaluated CAC quantification derived with a DL model, including correlation with expert annotations and associations with major adverse cardiovascular events (MACE). Methods: We trained a convolutional long short-term memory DL model to automatically quantify CAC on CTAC scans using 6,608 studies (2 centers) and evaluated the model in an external cohort of patients without known coronary artery disease (n = 2,271) obtained in a separate center. We assessed agreement between DL and expert annotated CAC scores. We also assessed associations between MACE (death, revascularization, myocardial infarction, or unstable angina) and CAC categories (0, 1-100, 101-400, or >400) for scores manually derived by experienced readers and scores obtained fully automatically by DL using multivariable Cox models (adjusted for age, sex, past medical history, perfusion, and ejection fraction) and net reclassification index. Results: In the external testing population, DL CAC was 0 in 908 patients (40.0%), 1-100 in 596 (26.2%), 100-400 in 354 (15.6%), and >400 in 413 (18.2%). Agreement in CAC category by DL CAC and expert annotation was excellent (linear weighted κ, 0.80), but DL CAC was obtained automatically in less than 2 s compared with about 2.5 min for expert CAC. DL CAC category was an independent risk factor for MACE with hazard ratios in comparison to a CAC of zero: CAC of 1-100 (2.20; 95% CI, 1.54-3.14; P < 0.001), CAC of 101-400 (4.58; 95% CI, 3.23-6.48; P < 0.001), and CAC of more than 400 (5.92; 95% CI, 4.27-8.22; P < 0.001). Overall, the net reclassification index was 0.494 for DL CAC, which was similar to expert annotated CAC (0.503). Conclusion: DL CAC from SPECT/CT attenuation maps agrees well with expert CAC annotations and provides a similar risk stratification but can be obtained automatically. DL CAC scores improved classification of a significant proportion of patients as compared with SPECT myocardial perfusion alone.
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Affiliation(s)
- Robert J H Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
- Department of Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Konrad Pieszko
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
- Department of Interventional Cardiology and Cardiac Surgery, University of Zielona Góra, Zielona Góra, Poland
| | - Aakash Shanbhag
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Attila Feher
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut; and
| | - Mark Lemley
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Aditya Killekar
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Paul B Kavanagh
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Serge D Van Kriekinge
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Joanna X Liang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Cathleen Huang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut; and
| | - Timothy Bateman
- Cardiovascular Imaging Technologies LLC, Kansas City, Missouri
| | - Daniel S Berman
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Damini Dey
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Piotr J Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California;
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Zair AM, Bouzouad Cherfa A, Cherfa Y, Belkhamsa N. An automated segmentation of coronary artery calcification using deep learning in specific region limitation. Med Biol Eng Comput 2023:10.1007/s11517-023-02797-z. [PMID: 36871109 DOI: 10.1007/s11517-023-02797-z] [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: 08/20/2021] [Accepted: 01/31/2023] [Indexed: 03/06/2023]
Abstract
Coronary artery calcification (CAC) is a frequent disease of the arteries that supply the surface of the heart muscle. Leaving a severe disease untreated can make it permanent. Computer tomography (CT), which is well known for its ability to quantify the Agatston score, is used to visualize high-resolution CACs. CAC segmentation is still an important topic. Our goal is to automatically segment CAC in a specific area and measure the Agatston score in 2D images. The heart region is limited using a threshold, unused structures are removed using 2D connectivity (muscle, lung, ribcage), the heart cavity is extracted using the convex hull of the lungs, and the CAC is then segmented in 2D using a convolutional neural network (U-Net models/SegNet-VGG16 with transfer learning). The Agatston score prediction is calculated for CAC quantification. The proposed strategy is tested through experiments, which yield encouraging outcomes. Graphical Abstract Deep learning for CAC segmentation in CT images.
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Affiliation(s)
- Asmae Mama Zair
- University of Blida 01, B.P 270, Soumaa road, Blida, Algeria.
| | | | - Yazid Cherfa
- University of Blida 01, B.P 270, Soumaa road, Blida, Algeria
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Suh YJ, Kim C, Lee JG, Oh H, Kang H, Kim YH, Yang DH. Fully automatic coronary calcium scoring in non-ECG-gated low-dose chest CT: comparison with ECG-gated cardiac CT. Eur Radiol 2023; 33:1254-1265. [PMID: 36098798 DOI: 10.1007/s00330-022-09117-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 07/05/2022] [Accepted: 08/15/2022] [Indexed: 02/04/2023]
Abstract
OBJECTIVES To validate an artificial intelligence (AI)-based fully automatic coronary artery calcium (CAC) scoring system on non-electrocardiogram (ECG)-gated low-dose chest computed tomography (LDCT) using multi-institutional datasets with manual CAC scoring as the reference standard. METHODS This retrospective study included 452 subjects from three academic institutions, who underwent both ECG-gated calcium scoring computed tomography (CSCT) and LDCT scans. For all CSCT and LDCT scans, automatic CAC scoring (CAC_auto) was performed using AI-based software, and manual CAC scoring (CAC_man) was set as the reference standard. The reliability and agreement of CAC_auto was evaluated and compared with that of CAC_man using intraclass correlation coefficients (ICCs) and Bland-Altman plots. The reliability between CAC_auto and CAC_man for CAC severity categories was analyzed using weighted kappa (κ) statistics. RESULTS CAC_auto on CSCT and LDCT yielded a high ICC (0.998, 95% confidence interval (CI) 0.998-0.999 and 0.989, 95% CI 0.987-0.991, respectively) and a mean difference with 95% limits of agreement of 1.3 ± 37.1 and 0.8 ± 75.7, respectively. CAC_auto achieved excellent reliability for CAC severity (κ = 0.918-0.972) on CSCT and good to excellent but heterogenous reliability among datasets (κ = 0.748-0.924) on LDCT. CONCLUSIONS The application of an AI-based automatic CAC scoring software to LDCT shows good to excellent reliability in CAC score and CAC severity categorization in multi-institutional datasets; however, the reliability varies among institutions. KEY POINTS • AI-based automatic CAC scoring on LDCT shows excellent reliability with manual CAC scoring in multi-institutional datasets. • The reliability for CAC score-based severity categorization varies among datasets. • Automatic scoring for LDCT shows a higher false-positive rate than automatic scoring for CSCT, and most common causes of a false-positive are image noise and artifacts for both CSCT and LDCT.
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Affiliation(s)
- Young Joo Suh
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Cherry Kim
- Department of Radiology, Korea University Ansan Hospital, Ansan, South Korea
| | - June-Goo Lee
- Biomedical Engineering Research Center, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Hongmin Oh
- Department of Radiology and Research Institute of Radiology, Cardiac Imaging Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Heejun Kang
- Divison of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Young-Hak Kim
- Divison of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Dong Hyun Yang
- Department of Radiology and Research Institute of Radiology, Cardiac Imaging Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
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Föllmer B, Biavati F, Wald C, Stober S, Ma J, Dewey M, Samek W. Active multitask learning with uncertainty-weighted loss for coronary calcium scoring. Med Phys 2022; 49:7262-7277. [PMID: 35861655 DOI: 10.1002/mp.15870] [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: 11/18/2021] [Revised: 06/01/2021] [Accepted: 06/20/2022] [Indexed: 01/01/2023] Open
Abstract
PURPOSE The coronary artery calcification (CAC) score is an independent marker for the risk of cardiovascular events. Automatic methods for quantifying CAC could reduce workload and assist radiologists in clinical decision-making. However, large annotated datasets are needed for training to achieve very good model performance, which is an expensive process and requires expert knowledge. The number of training data required can be reduced in an active learning scenario, which requires only the most informative samples to be labeled. Multitask learning techniques can improve model performance by joint learning of multiple related tasks and extraction of shared informative features. METHODS We propose an uncertainty-weighted multitask learning model for coronary calcium scoring in electrocardiogram-gated (ECG-gated), noncontrast-enhanced cardiac calcium scoring CT. The model was trained to solve the two tasks of coronary artery region segmentation (weak labels) and coronary artery calcification segmentation (strong labels) simultaneously in an active learning scenario to improve model performance and reduce the number of samples needed for training. We compared our model with a single-task U-Net and a sequential-task model as well as other state-of-the-art methods. The model was evaluated on 1275 individual patients in three different datasets (DISCHARGE, CADMAN, orCaScore), and the relationship between model performance and various influencing factors (image noise, metal artifacts, motion artifacts, image quality) was analyzed. RESULTS Joint learning of multiclass coronary artery region segmentation and binary coronary calcium segmentation improved calcium scoring performance. Since shared information can be learned from both tasks for complementary purposes, the model reached optimal performance with only 12% of the training data and one-third of the labeling time in an active learning scenario. We identified image noise as one of the most important factors influencing model performance along with anatomical abnormalities and metal artifacts. CONCLUSIONS Our multitask learning approach with uncertainty-weighted loss improves calcium scoring performance by joint learning of shared features and reduces labeling costs when trained in an active learning scenario.
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Affiliation(s)
- Bernhard Föllmer
- Department of Radiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Federico Biavati
- Department of Radiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Christian Wald
- Department of Radiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sebastian Stober
- Artificial Intelligence Lab, Otto-von-Guericke-Universität, Magdeburg, Germany
| | - Jackie Ma
- Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany
| | - Marc Dewey
- Department of Radiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.,Berlin Institute of Health and DZHK (German Centre for Cardiovascular Research), Berlin, Germany
| | - Wojciech Samek
- Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany
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Adikari D, Gharleghi R, Zhang S, Jorm L, Sowmya A, Moses D, Ooi SY, Beier S. A new and automated risk prediction of coronary artery disease using clinical endpoints and medical imaging-derived patient-specific insights: protocol for the retrospective GeoCAD cohort study. BMJ Open 2022; 12:e054881. [PMID: 35725256 PMCID: PMC9214399 DOI: 10.1136/bmjopen-2021-054881] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
INTRODUCTION Coronary artery disease (CAD) is the leading cause of death worldwide. More than a quarter of cardiovascular events are unexplained by current absolute cardiovascular disease risk calculators, and individuals without clinical risk factors have been shown to have worse outcomes. The 'anatomy of risk' hypothesis recognises that adverse anatomical features of coronary arteries enhance atherogenic haemodynamics, which in turn mediate the localisation and progression of plaques. We propose a new risk prediction method predicated on CT coronary angiography (CTCA) data and state-of-the-art machine learning methods based on a better understanding of anatomical risk for CAD. This may open new pathways in the early implementation of personalised preventive therapies in susceptible individuals as a potential key in addressing the growing burden of CAD. METHODS AND ANALYSIS GeoCAD is a retrospective cohort study in 1000 adult patients who have undergone CTCA for investigation of suspected CAD. It is a proof-of-concept study to test the hypothesis that advanced image-derived patient-specific data can accurately predict long-term cardiovascular events. The objectives are to (1) profile CTCA images with respect to variations in anatomical shape and associated haemodynamic risk expressing, at least in part, an individual's CAD risk, (2) develop a machine-learning algorithm for the rapid assessment of anatomical risk directly from unprocessed CTCA images and (3) to build a novel CAD risk model combining traditional risk factors with these novel anatomical biomarkers to provide a higher accuracy CAD risk prediction tool. ETHICS AND DISSEMINATION The study protocol has been approved by the St Vincent's Hospital Human Research Ethics Committee, Sydney-2020/ETH02127 and the NSW Population and Health Service Research Ethics Committee-2021/ETH00990. The project outcomes will be published in peer-reviewed and biomedical journals, scientific conferences and as a higher degree research thesis.
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Affiliation(s)
- Dona Adikari
- Faculty of Medicine, The University of New South Wales, Sydney, New South Wales, Australia
- Cardiology Department, The Prince of Wales Hospital, Sydney, New South Wales, Australia
| | - Ramtin Gharleghi
- School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, New South Wales, Australia
| | - Shisheng Zhang
- School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, New South Wales, Australia
| | - Louisa Jorm
- Centre for Big Data Research in Health, The University of New South Wales, Sydney, New South Wales, Australia
| | - Arcot Sowmya
- School of Computer Science and Engineering, The University of New South Wales, Sydney, New South Wales, Australia
| | - Daniel Moses
- School of Computer Science and Engineering, The University of New South Wales, Sydney, New South Wales, Australia
- Department of Medical Imaging, The Prince of Wales Hospital, Sydney, New South Wales, Australia
| | - Sze-Yuan Ooi
- Faculty of Medicine, The University of New South Wales, Sydney, New South Wales, Australia
- Cardiology Department, The Prince of Wales Hospital, Sydney, New South Wales, Australia
| | - Susann Beier
- School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, New South Wales, Australia
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Zair AM, Bouzouad Cherfa A, Cherfa Y, Belkhamsa N. Machine learning for coronary artery calcification detection and labeling using only native computer tomography. Phys Eng Sci Med 2021; 45:49-61. [PMID: 34792761 DOI: 10.1007/s13246-021-01080-5] [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: 01/09/2021] [Accepted: 11/10/2021] [Indexed: 11/26/2022]
Abstract
In recent decades, the World Health Organization has found an increase in the death rate due to cardiovascular disease. Calcifications of the coronary arteries are the main sign of any cardiovascular event. Each individual's calcium score helps estimate the severity of the disease. However, the score for each artery is more significant. This study aims to research the segmentation, the labeling, and then the complete and partial quantification of calcium using only native coronary computed tomography with the help of machine-learning algorithms. Our semi-automatic system limited the region of interest by applying a defined preprocessing step. We then implemented two random forest classifiers; the first separated true coronary artery calcification (CAC) from the noise, and the second labeled CAC into the right coronary artery, left coronary artery, left anterior descending artery, and left circumflex artery using specific features. Agatston score and volume score of each CAC, each artery, and all of the arteries were calculated. This method gave promising results, comparable to those found in the literature, with the accuracy of 99.98% and 100% for CAC detection and labeling respectively.
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Affiliation(s)
- Asmae Mama Zair
- LASICOM Laboratory, Department of Electronics, Faculty of Technology, University of Blida 1, 09000, Blida, Algeria.
| | - Assia Bouzouad Cherfa
- LASICOM Laboratory, Department of Electronics, Faculty of Technology, University of Blida 1, 09000, Blida, Algeria
| | - Yazid Cherfa
- LASICOM Laboratory, Department of Electronics, Faculty of Technology, University of Blida 1, 09000, Blida, Algeria
| | - Noureddine Belkhamsa
- LASICOM Laboratory, Department of Electronics, Faculty of Technology, University of Blida 1, 09000, Blida, Algeria
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Lauzier PT, Avram R, Dey D, Slomka P, Afilalo J, Chow BJ. The evolving role of artificial intelligence in cardiac image analysis. Can J Cardiol 2021; 38:214-224. [PMID: 34619340 DOI: 10.1016/j.cjca.2021.09.030] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 09/28/2021] [Accepted: 09/28/2021] [Indexed: 12/13/2022] Open
Abstract
Research in artificial intelligence (AI) have progressed over the last decade. The field of cardiac imaging has seen significant developments using newly developed deep learning methods for automated image analysis and AI tools for disease detection and prognostication. This review article is aimed at those without special background in AI. We review AI concepts and we survey the growing contemporary applications of AI for image analysis in echocardiography, nuclear cardiology, cardiac computed tomography, cardiac magnetic resonance, and invasive angiography.
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Affiliation(s)
| | - Robert Avram
- University of Ottawa Heart Institute, Ottawa, ON, Canada; Montreal Heart Institute, Montreal, QC, Canada
| | - Damini Dey
- Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Piotr Slomka
- Cedars-Sinai Medical Center, Los Angeles, CA, USA
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12
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Lee JG, Kim H, Kang H, Koo HJ, Kang JW, Kim YH, Yang DH. Fully Automatic Coronary Calcium Score Software Empowered by Artificial Intelligence Technology: Validation Study Using Three CT Cohorts. Korean J Radiol 2021; 22:1764-1776. [PMID: 34402248 PMCID: PMC8546141 DOI: 10.3348/kjr.2021.0148] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 04/26/2021] [Accepted: 05/13/2021] [Indexed: 11/26/2022] Open
Abstract
Objective This study aimed to validate a deep learning-based fully automatic calcium scoring (coronary artery calcium [CAC]_auto) system using previously published cardiac computed tomography (CT) cohort data with the manually segmented coronary calcium scoring (CAC_hand) system as the reference standard. Materials and Methods We developed the CAC_auto system using 100 co-registered, non-enhanced and contrast-enhanced CT scans. For the validation of the CAC_auto system, three previously published CT cohorts (n = 2985) were chosen to represent different clinical scenarios (i.e., 2647 asymptomatic, 220 symptomatic, 118 valve disease) and four CT models. The performance of the CAC_auto system in detecting coronary calcium was determined. The reliability of the system in measuring the Agatston score as compared with CAC_hand was also evaluated per vessel and per patient using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. The agreement between CAC_auto and CAC_hand based on the cardiovascular risk stratification categories (Agatston score: 0, 1–10, 11–100, 101–400, > 400) was evaluated. Results In 2985 patients, 6218 coronary calcium lesions were identified using CAC_hand. The per-lesion sensitivity and false-positive rate of the CAC_auto system in detecting coronary calcium were 93.3% (5800 of 6218) and 0.11 false-positive lesions per patient, respectively. The CAC_auto system, in measuring the Agatston score, yielded ICCs of 0.99 for all the vessels (left main 0.91, left anterior descending 0.99, left circumflex 0.96, right coronary 0.99). The limits of agreement between CAC_auto and CAC_hand were 1.6 ± 52.2. The linearly weighted kappa value for the Agatston score categorization was 0.94. The main causes of false-positive results were image noise (29.1%, 97/333 lesions), aortic wall calcification (25.5%, 85/333 lesions), and pericardial calcification (24.3%, 81/333 lesions). Conclusion The atlas-based CAC_auto empowered by deep learning provided accurate calcium score measurement as compared with manual method and risk category classification, which could potentially streamline CAC imaging workflows.
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Affiliation(s)
- June Goo Lee
- Biomedical Engineering Research Center, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - HeeSoo Kim
- Department of Radiology and Research Institute of Radiology, Cardiac Imaging Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Heejun Kang
- Divison of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Hyun Jung Koo
- Department of Radiology and Research Institute of Radiology, Cardiac Imaging Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Joon Won Kang
- Department of Radiology and Research Institute of Radiology, Cardiac Imaging Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Young Hak Kim
- Divison of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
| | - Dong Hyun Yang
- Department of Radiology and Research Institute of Radiology, Cardiac Imaging Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
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13
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Zhang N, Yang G, Zhang W, Wang W, Zhou Z, Zhang H, Xu L, Chen Y. Fully automatic framework for comprehensive coronary artery calcium scores analysis on non-contrast cardiac-gated CT scan: Total and vessel-specific quantifications. Eur J Radiol 2021; 134:109420. [PMID: 33302029 PMCID: PMC7814341 DOI: 10.1016/j.ejrad.2020.109420] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 09/27/2020] [Accepted: 11/14/2020] [Indexed: 12/31/2022]
Abstract
OBJECTIVES To develop a fully automatic multiview shape constraint framework for comprehensive coronary artery calcium scores (CACS) quantification via deep learning on nonenhanced cardiac CT images. METHODS In this retrospective single-centre study, a multi-task deep learning framework was proposed to detect and quantify coronary artery calcification from CT images collected between October 2018 and March 2019. A total of 232 non-contrast cardiac-gated CT scans were retrieved and studied (80 % for model training and 20 % for testing). CACS results of testing datasets (n = 46), including Agatston score, calcium volume score, calcium mass score, were calculated fully automatically and manually at total and vessel-specific levels, respectively. RESULTS No significant differences were found in CACS quantification obtained using automatic or manual methods at total and vessel-specific levels (Agatston score: automatic 535.3 vs. manual 542.0, P = 0.993; calcium volume score: automatic 454.2 vs. manual 460.6, P = 0.990; calcium mass score: automatic 128.9 vs. manual 129.5, P = 0.992). Compared to the ground truth, the number of calcified vessels can be accurate recognized automatically (total: automatic 107 vs. manual 102, P = 0.125; left main artery: automatic 15 vs. manual 14, P = 1.000 ; left ascending artery: automatic 37 vs. manual 37, P = 1.000; left circumflex artery: automatic 22 vs. manual 20, P = 0.625; right coronary artery: automatic 33 vs. manual 31, P = 0.500). At the patient's level, there was no statistic difference existed in the classification of Agatston scoring (P = 0.317) and the number of calcified vessels (P = 0.102) between the automatic and manual results. CONCLUSIONS The proposed framework can achieve reliable and comprehensive quantification for the CACS, including the calcified extent and distribution indicators at both total and vessel-specific levels.
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Affiliation(s)
- Nan Zhang
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 2(nd) Anzhen Road, Chaoyang District, Beijing, China
| | - Guang Yang
- Cardiovascular Research Centre, Royal Brompton Hospital, SW3 6NP, London, UK; National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
| | - Weiwei Zhang
- School of Biomedical Engineering, Sun Yat-Sen University, China
| | - Wenjing Wang
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 2(nd) Anzhen Road, Chaoyang District, Beijing, China
| | - Zhen Zhou
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 2(nd) Anzhen Road, Chaoyang District, Beijing, China
| | - Heye Zhang
- School of Biomedical Engineering, Sun Yat-Sen University, China
| | - Lei Xu
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 2(nd) Anzhen Road, Chaoyang District, Beijing, China.
| | - Yundai Chen
- Department of Cardiology, Chinese PLA General Hospital, Beijing, China
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14
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Artificial Intelligence in Cardiac CT: Automated Calcium Scoring and Plaque Analysis. CURRENT CARDIOVASCULAR IMAGING REPORTS 2020. [DOI: 10.1007/s12410-020-09549-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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15
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Lee H, Martin S, Burt JR, Bagherzadeh PS, Rapaka S, Gray HN, Leonard TJ, Schwemmer C, Schoepf UJ. Machine Learning and Coronary Artery Calcium Scoring. Curr Cardiol Rep 2020; 22:90. [PMID: 32647932 DOI: 10.1007/s11886-020-01337-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
PURPOSE OF REVIEW To summarize current artificial intelligence (AI)-based applications for coronary artery calcium scoring (CACS) and their potential clinical impact. RECENT FINDINGS Recent evolution of AI-based technologies in medical imaging has accelerated progress in CACS performed in diverse types of CT examinations, providing promising results for future clinical application in this field. CACS plays a key role in risk stratification of coronary artery disease (CAD) and patient management. Recent emergence of AI algorithms, particularly deep learning (DL)-based applications, have provided considerable progress in CACS. Many investigations have focused on the clinical role of DL models in CACS and showed excellent agreement between those algorithms and manual scoring, not only in dedicated coronary calcium CT but also in coronary CT angiography (CCTA), low-dose chest CT, and standard chest CT. Therefore, the potential of AI-based CACS may become more influential in the future.
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Affiliation(s)
- Heon Lee
- Department of Radiology, Soonchunhyang University Hospital Bucheon, 170 Jomaru-ro, Bucheon, 14584, Republic of Korea
| | - Simon Martin
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC, 29425, USA
| | - Jeremy R Burt
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC, 29425, USA
| | | | - Saikiran Rapaka
- Siemens Healthcare GmbH, Siemensstr. 3, 91301, Forchheim, Germany
| | - Hunter N Gray
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC, 29425, USA
| | - Tyler J Leonard
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC, 29425, USA
| | - Chris Schwemmer
- Siemens Healthcare GmbH, Siemensstr. 3, 91301, Forchheim, Germany
| | - U Joseph Schoepf
- Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC, 29425, USA.
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16
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Slomka PJ, Miller RJH, Isgum I, Dey D. Application and Translation of Artificial Intelligence to Cardiovascular Imaging in Nuclear Medicine and Noncontrast CT. Semin Nucl Med 2020; 50:357-366. [DOI: 10.1053/j.semnuclmed.2020.03.004] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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17
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Zhao FJ, Fan SQ, Ren JF, von Deneen KM, He XW, Chen XL. Machine learning for diagnosis of coronary artery disease in computed tomography angiography: A survey. Artif Intell Med Imaging 2020; 1:31-39. [DOI: 10.35711/aimi.v1.i1.31] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 06/12/2020] [Accepted: 06/16/2020] [Indexed: 02/06/2023] Open
Abstract
Coronary artery disease (CAD) has become a major illness endangering human health. It mainly manifests as atherosclerotic plaques, especially vulnerable plaques without obvious symptoms in the early stage. Once a rupture occurs, it will lead to severe coronary stenosis, which in turn may trigger a major adverse cardiovascular event. Computed tomography angiography (CTA) has become a standard diagnostic tool for early screening of coronary plaque and stenosis due to its advantages in high resolution, noninvasiveness, and three-dimensional imaging. However, manual examination of CTA images by radiologists has been proven to be tedious and time-consuming, which might also lead to intra- and interobserver errors. Nowadays, many machine learning algorithms have enabled the (semi-)automatic diagnosis of CAD by extracting quantitative features from CTA images. This paper provides a survey of these machine learning algorithms for the diagnosis of CAD in CTA images, including coronary artery extraction, coronary plaque detection, vulnerable plaque identification, and coronary stenosis assessment. Most included articles were published within this decade and are found in the Web of Science. We wish to give readers a glimpse of the current status, challenges, and perspectives of these machine learning-based analysis methods for automatic CAD diagnosis.
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Affiliation(s)
- Feng-Jun Zhao
- School of Information Science and Technology, Northwest University, Xi’an 710069, Shaanxi Province, China
- Xi’an Key Lab of Radiomics and Intelligent Perception, Northwest University, Xi’an 710069, Shaanxi Province, China
| | - Si-Qi Fan
- School of Information Science and Technology, Northwest University, Xi’an 710069, Shaanxi Province, China
- Xi’an Key Lab of Radiomics and Intelligent Perception, Northwest University, Xi’an 710069, Shaanxi Province, China
| | - Jing-Fang Ren
- School of Information Science and Technology, Northwest University, Xi’an 710069, Shaanxi Province, China
- Xi’an Key Lab of Radiomics and Intelligent Perception, Northwest University, Xi’an 710069, Shaanxi Province, China
| | - Karen M von Deneen
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an 710126, Shaanxi Province, China
| | - Xiao-Wei He
- School of Information Science and Technology, Northwest University, Xi’an 710069, Shaanxi Province, China
- Xi’an Key Lab of Radiomics and Intelligent Perception, Northwest University, Xi’an 710069, Shaanxi Province, China
| | - Xue-Li Chen
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an 710126, Shaanxi Province, China
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18
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van Velzen SGM, Lessmann N, Velthuis BK, Bank IEM, van den Bongard DHJG, Leiner T, de Jong PA, Veldhuis WB, Correa A, Terry JG, Carr JJ, Viergever MA, Verkooijen HM, Išgum I. Deep Learning for Automatic Calcium Scoring in CT: Validation Using Multiple Cardiac CT and Chest CT Protocols. Radiology 2020; 295:66-79. [PMID: 32043947 PMCID: PMC7106943 DOI: 10.1148/radiol.2020191621] [Citation(s) in RCA: 122] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 11/16/2019] [Accepted: 12/12/2019] [Indexed: 12/19/2022]
Abstract
Background Although several deep learning (DL) calcium scoring methods have achieved excellent performance for specific CT protocols, their performance in a range of CT examination types is unknown. Purpose To evaluate the performance of a DL method for automatic calcium scoring across a wide range of CT examination types and to investigate whether the method can adapt to different types of CT examinations when representative images are added to the existing training data set. Materials and Methods The study included 7240 participants who underwent various types of nonenhanced CT examinations that included the heart: coronary artery calcium (CAC) scoring CT, diagnostic CT of the chest, PET attenuation correction CT, radiation therapy treatment planning CT, CAC screening CT, and low-dose CT of the chest. CAC and thoracic aorta calcification (TAC) were quantified using a convolutional neural network trained with (a) 1181 low-dose chest CT examinations (baseline), (b) a small set of examinations of the respective type supplemented to the baseline (data specific), and (c) a combination of examinations of all available types (combined). Supplemental training sets contained 199-568 CT images depending on the calcium burden of each population. The DL algorithm performance was evaluated with intraclass correlation coefficients (ICCs) between DL and manual (Agatston) CAC and (volume) TAC scoring and with linearly weighted κ values for cardiovascular risk categories (Agatston score; cardiovascular disease risk categories: 0, 1-10, 11-100, 101-400, >400). Results At baseline, the DL algorithm yielded ICCs of 0.79-0.97 for CAC and 0.66-0.98 for TAC across the range of different types of CT examinations. ICCs improved to 0.84-0.99 (CAC) and 0.92-0.99 (TAC) for CT protocol-specific training and to 0.85-0.99 (CAC) and 0.96-0.99 (TAC) for combined training. For assignment of cardiovascular disease risk category, the κ value for all test CT scans was 0.90 (95% confidence interval [CI]: 0.89, 0.91) for the baseline training. It increased to 0.92 (95% CI: 0.91, 0.93) for both data-specific and combined training. Conclusion A deep learning calcium scoring algorithm for quantification of coronary and thoracic calcium was robust, despite substantial differences in CT protocol and variations in subject population. Augmenting the algorithm training with CT protocol-specific images further improved algorithm performance. © RSNA, 2020 See also the editorial by Vannier in this issue.
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Affiliation(s)
- Sanne G. M. van Velzen
- From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.),
Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental
Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division
(H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX
Utrecht, the Netherlands; Department of Radiology and Nuclear Medicine, Radboud
University Medical Center, Nijmegen, the Netherlands (N.L.); Departments of
Biomedical Engineering and Physics (S.G.M.v.V., I.I.) and Radiology and Nuclear
Medicine (I.I.), and Amsterdam Cardiovascular Sciences (I.I.), Amsterdam
University Medical Center, University of Amsterdam, the Netherlands; Department
of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen,
the Netherlands (N.L.); Department of Cardiology, Meander Medical Center,
Amersfoort, the Netherlands (I.E.M.B.); Department of Medicine, University of
Mississippi Medical Center, Jackson, Miss (A.C.); and Department of Radiology
and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tenn
(J.G.T., J.J.C.)
| | - Nikolas Lessmann
- From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.),
Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental
Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division
(H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX
Utrecht, the Netherlands; Department of Radiology and Nuclear Medicine, Radboud
University Medical Center, Nijmegen, the Netherlands (N.L.); Departments of
Biomedical Engineering and Physics (S.G.M.v.V., I.I.) and Radiology and Nuclear
Medicine (I.I.), and Amsterdam Cardiovascular Sciences (I.I.), Amsterdam
University Medical Center, University of Amsterdam, the Netherlands; Department
of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen,
the Netherlands (N.L.); Department of Cardiology, Meander Medical Center,
Amersfoort, the Netherlands (I.E.M.B.); Department of Medicine, University of
Mississippi Medical Center, Jackson, Miss (A.C.); and Department of Radiology
and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tenn
(J.G.T., J.J.C.)
| | - Birgitta K. Velthuis
- From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.),
Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental
Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division
(H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX
Utrecht, the Netherlands; Department of Radiology and Nuclear Medicine, Radboud
University Medical Center, Nijmegen, the Netherlands (N.L.); Departments of
Biomedical Engineering and Physics (S.G.M.v.V., I.I.) and Radiology and Nuclear
Medicine (I.I.), and Amsterdam Cardiovascular Sciences (I.I.), Amsterdam
University Medical Center, University of Amsterdam, the Netherlands; Department
of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen,
the Netherlands (N.L.); Department of Cardiology, Meander Medical Center,
Amersfoort, the Netherlands (I.E.M.B.); Department of Medicine, University of
Mississippi Medical Center, Jackson, Miss (A.C.); and Department of Radiology
and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tenn
(J.G.T., J.J.C.)
| | - Ingrid E. M. Bank
- From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.),
Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental
Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division
(H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX
Utrecht, the Netherlands; Department of Radiology and Nuclear Medicine, Radboud
University Medical Center, Nijmegen, the Netherlands (N.L.); Departments of
Biomedical Engineering and Physics (S.G.M.v.V., I.I.) and Radiology and Nuclear
Medicine (I.I.), and Amsterdam Cardiovascular Sciences (I.I.), Amsterdam
University Medical Center, University of Amsterdam, the Netherlands; Department
of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen,
the Netherlands (N.L.); Department of Cardiology, Meander Medical Center,
Amersfoort, the Netherlands (I.E.M.B.); Department of Medicine, University of
Mississippi Medical Center, Jackson, Miss (A.C.); and Department of Radiology
and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tenn
(J.G.T., J.J.C.)
| | - Desiree H. J. G. van den Bongard
- From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.),
Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental
Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division
(H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX
Utrecht, the Netherlands; Department of Radiology and Nuclear Medicine, Radboud
University Medical Center, Nijmegen, the Netherlands (N.L.); Departments of
Biomedical Engineering and Physics (S.G.M.v.V., I.I.) and Radiology and Nuclear
Medicine (I.I.), and Amsterdam Cardiovascular Sciences (I.I.), Amsterdam
University Medical Center, University of Amsterdam, the Netherlands; Department
of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen,
the Netherlands (N.L.); Department of Cardiology, Meander Medical Center,
Amersfoort, the Netherlands (I.E.M.B.); Department of Medicine, University of
Mississippi Medical Center, Jackson, Miss (A.C.); and Department of Radiology
and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tenn
(J.G.T., J.J.C.)
| | - Tim Leiner
- From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.),
Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental
Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division
(H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX
Utrecht, the Netherlands; Department of Radiology and Nuclear Medicine, Radboud
University Medical Center, Nijmegen, the Netherlands (N.L.); Departments of
Biomedical Engineering and Physics (S.G.M.v.V., I.I.) and Radiology and Nuclear
Medicine (I.I.), and Amsterdam Cardiovascular Sciences (I.I.), Amsterdam
University Medical Center, University of Amsterdam, the Netherlands; Department
of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen,
the Netherlands (N.L.); Department of Cardiology, Meander Medical Center,
Amersfoort, the Netherlands (I.E.M.B.); Department of Medicine, University of
Mississippi Medical Center, Jackson, Miss (A.C.); and Department of Radiology
and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tenn
(J.G.T., J.J.C.)
| | - Pim A. de Jong
- From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.),
Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental
Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division
(H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX
Utrecht, the Netherlands; Department of Radiology and Nuclear Medicine, Radboud
University Medical Center, Nijmegen, the Netherlands (N.L.); Departments of
Biomedical Engineering and Physics (S.G.M.v.V., I.I.) and Radiology and Nuclear
Medicine (I.I.), and Amsterdam Cardiovascular Sciences (I.I.), Amsterdam
University Medical Center, University of Amsterdam, the Netherlands; Department
of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen,
the Netherlands (N.L.); Department of Cardiology, Meander Medical Center,
Amersfoort, the Netherlands (I.E.M.B.); Department of Medicine, University of
Mississippi Medical Center, Jackson, Miss (A.C.); and Department of Radiology
and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tenn
(J.G.T., J.J.C.)
| | - Wouter B. Veldhuis
- From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.),
Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental
Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division
(H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX
Utrecht, the Netherlands; Department of Radiology and Nuclear Medicine, Radboud
University Medical Center, Nijmegen, the Netherlands (N.L.); Departments of
Biomedical Engineering and Physics (S.G.M.v.V., I.I.) and Radiology and Nuclear
Medicine (I.I.), and Amsterdam Cardiovascular Sciences (I.I.), Amsterdam
University Medical Center, University of Amsterdam, the Netherlands; Department
of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen,
the Netherlands (N.L.); Department of Cardiology, Meander Medical Center,
Amersfoort, the Netherlands (I.E.M.B.); Department of Medicine, University of
Mississippi Medical Center, Jackson, Miss (A.C.); and Department of Radiology
and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tenn
(J.G.T., J.J.C.)
| | - Adolfo Correa
- From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.),
Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental
Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division
(H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX
Utrecht, the Netherlands; Department of Radiology and Nuclear Medicine, Radboud
University Medical Center, Nijmegen, the Netherlands (N.L.); Departments of
Biomedical Engineering and Physics (S.G.M.v.V., I.I.) and Radiology and Nuclear
Medicine (I.I.), and Amsterdam Cardiovascular Sciences (I.I.), Amsterdam
University Medical Center, University of Amsterdam, the Netherlands; Department
of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen,
the Netherlands (N.L.); Department of Cardiology, Meander Medical Center,
Amersfoort, the Netherlands (I.E.M.B.); Department of Medicine, University of
Mississippi Medical Center, Jackson, Miss (A.C.); and Department of Radiology
and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tenn
(J.G.T., J.J.C.)
| | - James G. Terry
- From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.),
Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental
Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division
(H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX
Utrecht, the Netherlands; Department of Radiology and Nuclear Medicine, Radboud
University Medical Center, Nijmegen, the Netherlands (N.L.); Departments of
Biomedical Engineering and Physics (S.G.M.v.V., I.I.) and Radiology and Nuclear
Medicine (I.I.), and Amsterdam Cardiovascular Sciences (I.I.), Amsterdam
University Medical Center, University of Amsterdam, the Netherlands; Department
of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen,
the Netherlands (N.L.); Department of Cardiology, Meander Medical Center,
Amersfoort, the Netherlands (I.E.M.B.); Department of Medicine, University of
Mississippi Medical Center, Jackson, Miss (A.C.); and Department of Radiology
and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tenn
(J.G.T., J.J.C.)
| | - John Jeffrey Carr
- From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.),
Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental
Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division
(H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX
Utrecht, the Netherlands; Department of Radiology and Nuclear Medicine, Radboud
University Medical Center, Nijmegen, the Netherlands (N.L.); Departments of
Biomedical Engineering and Physics (S.G.M.v.V., I.I.) and Radiology and Nuclear
Medicine (I.I.), and Amsterdam Cardiovascular Sciences (I.I.), Amsterdam
University Medical Center, University of Amsterdam, the Netherlands; Department
of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen,
the Netherlands (N.L.); Department of Cardiology, Meander Medical Center,
Amersfoort, the Netherlands (I.E.M.B.); Department of Medicine, University of
Mississippi Medical Center, Jackson, Miss (A.C.); and Department of Radiology
and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tenn
(J.G.T., J.J.C.)
| | - Max A. Viergever
- From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.),
Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental
Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division
(H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX
Utrecht, the Netherlands; Department of Radiology and Nuclear Medicine, Radboud
University Medical Center, Nijmegen, the Netherlands (N.L.); Departments of
Biomedical Engineering and Physics (S.G.M.v.V., I.I.) and Radiology and Nuclear
Medicine (I.I.), and Amsterdam Cardiovascular Sciences (I.I.), Amsterdam
University Medical Center, University of Amsterdam, the Netherlands; Department
of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen,
the Netherlands (N.L.); Department of Cardiology, Meander Medical Center,
Amersfoort, the Netherlands (I.E.M.B.); Department of Medicine, University of
Mississippi Medical Center, Jackson, Miss (A.C.); and Department of Radiology
and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tenn
(J.G.T., J.J.C.)
| | - Helena M. Verkooijen
- From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.),
Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental
Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division
(H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX
Utrecht, the Netherlands; Department of Radiology and Nuclear Medicine, Radboud
University Medical Center, Nijmegen, the Netherlands (N.L.); Departments of
Biomedical Engineering and Physics (S.G.M.v.V., I.I.) and Radiology and Nuclear
Medicine (I.I.), and Amsterdam Cardiovascular Sciences (I.I.), Amsterdam
University Medical Center, University of Amsterdam, the Netherlands; Department
of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen,
the Netherlands (N.L.); Department of Cardiology, Meander Medical Center,
Amersfoort, the Netherlands (I.E.M.B.); Department of Medicine, University of
Mississippi Medical Center, Jackson, Miss (A.C.); and Department of Radiology
and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tenn
(J.G.T., J.J.C.)
| | - Ivana Išgum
- From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.),
Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental
Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division
(H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX
Utrecht, the Netherlands; Department of Radiology and Nuclear Medicine, Radboud
University Medical Center, Nijmegen, the Netherlands (N.L.); Departments of
Biomedical Engineering and Physics (S.G.M.v.V., I.I.) and Radiology and Nuclear
Medicine (I.I.), and Amsterdam Cardiovascular Sciences (I.I.), Amsterdam
University Medical Center, University of Amsterdam, the Netherlands; Department
of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen,
the Netherlands (N.L.); Department of Cardiology, Meander Medical Center,
Amersfoort, the Netherlands (I.E.M.B.); Department of Medicine, University of
Mississippi Medical Center, Jackson, Miss (A.C.); and Department of Radiology
and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tenn
(J.G.T., J.J.C.)
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19
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Hampe N, Wolterink JM, van Velzen SGM, Leiner T, Išgum I. Machine Learning for Assessment of Coronary Artery Disease in Cardiac CT: A Survey. Front Cardiovasc Med 2019; 6:172. [PMID: 32039237 PMCID: PMC6988816 DOI: 10.3389/fcvm.2019.00172] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 11/12/2019] [Indexed: 01/10/2023] Open
Abstract
Cardiac computed tomography (CT) allows rapid visualization of the heart and coronary arteries with high spatial resolution. However, analysis of cardiac CT scans for manifestation of coronary artery disease is time-consuming and challenging. Machine learning (ML) approaches have the potential to address these challenges with high accuracy and consistent performance. In this mini review, we present a survey of the literature on ML-based analysis of coronary artery disease in cardiac CT. We summarize ML methods for detection and characterization of atherosclerotic plaque as well as anatomically and functionally significant coronary artery stenosis.
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Affiliation(s)
- Nils Hampe
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Jelmer M Wolterink
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Sanne G M van Velzen
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Tim Leiner
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands.,Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
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20
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Sandstedt M, Henriksson L, Janzon M, Nyberg G, Engvall J, De Geer J, Alfredsson J, Persson A. Evaluation of an AI-based, automatic coronary artery calcium scoring software. Eur Radiol 2019; 30:1671-1678. [PMID: 31728692 PMCID: PMC7033052 DOI: 10.1007/s00330-019-06489-x] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 09/26/2019] [Accepted: 10/09/2019] [Indexed: 11/04/2022]
Abstract
Objectives To evaluate an artificial intelligence (AI)–based, automatic coronary artery calcium (CAC) scoring software, using a semi-automatic software as a reference. Methods This observational study included 315 consecutive, non-contrast-enhanced calcium scoring computed tomography (CSCT) scans. A semi-automatic and an automatic software obtained the Agatston score (AS), the volume score (VS), the mass score (MS), and the number of calcified coronary lesions. Semi-automatic and automatic analysis time were registered, including a manual double-check of the automatic results. Statistical analyses were Spearman’s rank correlation coefficient (⍴), intra-class correlation (ICC), Bland Altman plots, weighted kappa analysis (κ), and Wilcoxon signed-rank test. Results The correlation and agreement for the AS, VS, and MS were ⍴ = 0.935, 0.932, 0.934 (p < 0.001), and ICC = 0.996, 0.996, 0.991, respectively (p < 0.001). The correlation and agreement for the number of calcified lesions were ⍴ = 0.903 and ICC = 0.977 (p < 0.001), respectively. The Bland Altman mean difference and 1.96 SD upper and lower limits of agreements for the AS, VS, and MS were − 8.2 (− 115.1 to 98.2), − 7.4 (− 93.9 to 79.1), and − 3.8 (− 33.6 to 25.9), respectively. Agreement in risk category assignment was 89.5% and κ = 0.919 (p < 0.001). The median time for the semi-automatic and automatic method was 59 s (IQR 35–100) and 36 s (IQR 29–49), respectively (p < 0.001). Conclusions There was an excellent correlation and agreement between the automatic software and the semi-automatic software for three CAC scores and the number of calcified lesions. Risk category classification was accurate but showing an overestimation bias tendency. Also, the automatic method was less time-demanding. Key Points • Coronary artery calcium (CAC) scoring is an excellent candidate for artificial intelligence (AI) development in a clinical setting. • An AI-based, automatic software obtained CAC scores with excellent correlation and agreement compared with a conventional method but was less time-consuming.
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Affiliation(s)
- Mårten Sandstedt
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden. .,Department of Radiology and Department of Medical and Health Sciences, University Hospital of Linköping, Linköping University, SE-581 85, Linköping, Sweden.
| | - Lilian Henriksson
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Radiology and Department of Medical and Health Sciences, University Hospital of Linköping, Linköping University, SE-581 85, Linköping, Sweden
| | - Magnus Janzon
- Department of Cardiology and Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Gusten Nyberg
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Radiology and Department of Medical and Health Sciences, University Hospital of Linköping, Linköping University, SE-581 85, Linköping, Sweden
| | - Jan Engvall
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Clinical Physiology and Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Jakob De Geer
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Radiology and Department of Medical and Health Sciences, University Hospital of Linköping, Linköping University, SE-581 85, Linköping, Sweden
| | - Joakim Alfredsson
- Department of Cardiology and Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
| | - Anders Persson
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.,Department of Radiology and Department of Medical and Health Sciences, University Hospital of Linköping, Linköping University, SE-581 85, Linköping, Sweden
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21
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Lessmann N, van Ginneken B, Zreik M, de Jong PA, de Vos BD, Viergever MA, Isgum I. Automatic Calcium Scoring in Low-Dose Chest CT Using Deep Neural Networks With Dilated Convolutions. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:615-625. [PMID: 29408789 DOI: 10.1109/tmi.2017.2769839] [Citation(s) in RCA: 132] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Heavy smokers undergoing screening with low-dose chest CT are affected by cardiovascular disease as much as by lung cancer. Low-dose chest CT scans acquired in screening enable quantification of atherosclerotic calcifications and thus enable identification of subjects at increased cardiovascular risk. This paper presents a method for automatic detection of coronary artery, thoracic aorta, and cardiac valve calcifications in low-dose chest CT using two consecutive convolutional neural networks. The first network identifies and labels potential calcifications according to their anatomical location and the second network identifies true calcifications among the detected candidates. This method was trained and evaluated on a set of 1744 CT scans from the National Lung Screening Trial. To determine whether any reconstruction or only images reconstructed with soft tissue filters can be used for calcification detection, we evaluated the method on soft and medium/sharp filter reconstructions separately. On soft filter reconstructions, the method achieved F1 scores of 0.89, 0.89, 0.67, and 0.55 for coronary artery, thoracic aorta, aortic valve, and mitral valve calcifications, respectively. On sharp filter reconstructions, the F1 scores were 0.84, 0.81, 0.64, and 0.66, respectively. Linearly weighted kappa coefficients for risk category assignment based on per subject coronary artery calcium were 0.91 and 0.90 for soft and sharp filter reconstructions, respectively. These results demonstrate that the presented method enables reliable automatic cardiovascular risk assessment in all low-dose chest CT scans acquired for lung cancer screening.
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22
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Priyatharshini R., Chitrakala S.. An Efficient Coronary Disease Diagnosis System Using Dual-Phase Multi-Objective Optimization and Embedded Feature Selection. INTERNATIONAL JOURNAL OF INTELLIGENT INFORMATION TECHNOLOGIES 2017. [DOI: 10.4018/ijiit.2017070102] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Developments in healthcare technologies have significantly enhanced spatial resolution and improved contrast resolution, permitting analysis of additional subtle structures than formerly attainable. An approach for Automatic recognition and quantification of calcifications from arteries in computed tomography (CT) scans is developed which is a key necessity in planning the treatment of individuals with suspected coronary artery disease. First, a Dual-Phase Multi-_objective Optimization approach using an Active Contour Model-based region-growing technique is developed. Second, an embedded feature selection method is developed with an expert classifier to detect calcified objects in the segmented artery with great accuracy. Finally, the Agatston scoring method is utilized to quantify the level of coronary artery calcium plaque. Coronary CT images from the AS+CT scanner with a slice thickness of 3 mm were obtained from clinical practice. Experimental results demonstrate that our proposed method improves the accuracy of lesion detection for better treatment planning.
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Affiliation(s)
- Priyatharshini R.
- Easwari Engineering College, Department of Information Technology, Chennai, India
| | - Chitrakala S.
- Anna University, Department of Computer Science and Engineering, Chennai, India
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23
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Wolterink JM, Leiner T, de Vos BD, Coatrieux JL, Kelm BM, Kondo S, Salgado RA, Shahzad R, Shu H, Snoeren M, Takx RAP, van Vliet LJ, van Walsum T, Willems TP, Yang G, Zheng Y, Viergever MA, Išgum I. An evaluation of automatic coronary artery calcium scoring methods with cardiac CT using the orCaScore framework. Med Phys 2017; 43:2361. [PMID: 27147348 DOI: 10.1118/1.4945696] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
PURPOSE The amount of coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular disease (CVD) events. In clinical practice, CAC is manually identified and automatically quantified in cardiac CT using commercially available software. This is a tedious and time-consuming process in large-scale studies. Therefore, a number of automatic methods that require no interaction and semiautomatic methods that require very limited interaction for the identification of CAC in cardiac CT have been proposed. Thus far, a comparison of their performance has been lacking. The objective of this study was to perform an independent evaluation of (semi)automatic methods for CAC scoring in cardiac CT using a publicly available standardized framework. METHODS Cardiac CT exams of 72 patients distributed over four CVD risk categories were provided for (semi)automatic CAC scoring. Each exam consisted of a noncontrast-enhanced calcium scoring CT (CSCT) and a corresponding coronary CT angiography (CCTA) scan. The exams were acquired in four different hospitals using state-of-the-art equipment from four major CT scanner vendors. The data were divided into 32 training exams and 40 test exams. A reference standard for CAC in CSCT was defined by consensus of two experts following a clinical protocol. The framework organizers evaluated the performance of (semi)automatic methods on test CSCT scans, per lesion, artery, and patient. RESULTS Five (semi)automatic methods were evaluated. Four methods used both CSCT and CCTA to identify CAC, and one method used only CSCT. The evaluated methods correctly detected between 52% and 94% of CAC lesions with positive predictive values between 65% and 96%. Lesions in distal coronary arteries were most commonly missed and aortic calcifications close to the coronary ostia were the most common false positive errors. The majority (between 88% and 98%) of correctly identified CAC lesions were assigned to the correct artery. Linearly weighted Cohen's kappa for patient CVD risk categorization by the evaluated methods ranged from 0.80 to 1.00. CONCLUSIONS A publicly available standardized framework for the evaluation of (semi)automatic methods for CAC identification in cardiac CT is described. An evaluation of five (semi)automatic methods within this framework shows that automatic per patient CVD risk categorization is feasible. CAC lesions at ambiguous locations such as the coronary ostia remain challenging, but their detection had limited impact on CVD risk determination.
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Affiliation(s)
- Jelmer M Wolterink
- Image Sciences Institute, University Medical Center Utrecht, Utrecht 3508 GA, The Netherlands
| | - Tim Leiner
- Department of Radiology, University Medical Center Utrecht, Utrecht 3508 GA, The Netherlands
| | - Bob D de Vos
- Image Sciences Institute, University Medical Center Utrecht, Utrecht 3508 GA, The Netherlands
| | - Jean-Louis Coatrieux
- INSERM, U1099, Rennes F-35000, France; LTSI, Université de Rennes 1, Rennes F-35000, France; and Centre de Recherche en Information Biomédicale Sino-Français (LIA CRIBs), Nanjing 210096, China
| | - B Michael Kelm
- Imaging and Computer Vision, Corporate Technology, Siemens AG, Erlangen 91051, Germany
| | | | - Rodrigo A Salgado
- Department of Radiology, University Hospital Antwerpen, Edegem 2650, Belgium
| | - Rahil Shahzad
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden 2300 RC, The Netherlands; Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam 3000 CA, The Netherlands; and Quantitative Imaging Group, Department of Imaging Physics, Faculty of Applied Sciences, Delft University of Technology, Delft 2600 GA, The Netherlands
| | - Huazhong Shu
- Centre de Recherche en Information Biomédicale Sino-Français (LIA CRIBs), Nanjing 210096, China and Lab of Image Science and Technology, School of Computer Science and Technology, Nanjing 210096, China
| | - Miranda Snoeren
- Department of Radiology, Radboud University Medical Center, Nijmegen 6500 HB, The Netherlands
| | - Richard A P Takx
- Department of Radiology, University Medical Center Utrecht, Utrecht 3508 GA, The Netherlands
| | - Lucas J van Vliet
- Quantitative Imaging Group, Department of Imaging Physics, Faculty of Applied Sciences, Delft University of Technology, Delft 2600 GA, The Netherlands
| | - Theo van Walsum
- Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam 3000 CA, The Netherlands
| | - Tineke P Willems
- Department of Radiology, University Medical Center Groningen, Groningen 9700 RB, The Netherlands
| | - Guanyu Yang
- Lab of Image Science and Technology, School of Computer Science and Technology, Nanjing 210096, China and Centre de Recherche en Information Biomédicale Sino-Français (LIA CRIBs), Nanjing 210096, China
| | - Yefeng Zheng
- Imaging and Computer Vision, Corporate Technology, Siemens Corporation, Princeton, New Jersey 08540-6632
| | - Max A Viergever
- Image Sciences Institute, University Medical Center Utrecht, Utrecht 3508 GA, The Netherlands
| | - Ivana Išgum
- Image Sciences Institute, University Medical Center Utrecht, Utrecht 3508 GA, The Netherlands
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24
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Automatic Coronary Artery Calcium Scoring on Radiotherapy Planning CT Scans of Breast Cancer Patients: Reproducibility and Association with Traditional Cardiovascular Risk Factors. PLoS One 2016; 11:e0167925. [PMID: 27936125 PMCID: PMC5148008 DOI: 10.1371/journal.pone.0167925] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2016] [Accepted: 11/22/2016] [Indexed: 01/07/2023] Open
Abstract
Objectives Coronary artery calcium (CAC) is a strong and independent predictor of cardiovascular disease (CVD) risk. This study assesses reproducibility of automatic CAC scoring on radiotherapy planning computed tomography (CT) scans of breast cancer patients, and examines its association with traditional cardiovascular risk factors. Methods This study included 561 breast cancer patients undergoing radiotherapy between 2013 and 2015. CAC was automatically scored with an algorithm using supervised pattern recognition, expressed as Agatston scores and categorized into five categories (0, 1–10, 11–100, 101–400, >400). Reproducibility between automatic and manual expert scoring was assessed in 79 patients with automatically determined CAC above zero and 84 randomly selected patients without automatically determined CAC. Interscan reproducibility of automatic scoring was assessed in 294 patients having received two scans (82% on the same day). Association between CAC and CVD risk factors was assessed in 36 patients with CAC scores >100, 72 randomly selected patients with scores 1–100, and 72 randomly selected patients without CAC. Reliability was assessed with linearly weighted kappa and agreement with proportional agreement. Results 134 out of 561 (24%) patients had a CAC score above zero. Reliability of CVD risk categorization between automatic and manual scoring was 0.80 (95% Confidence Interval (CI): 0.74–0.87), and slightly higher for scans with breath-hold. Agreement was 0.79 (95% CI: 0.72–0.85). Interscan reliability was 0.61 (95% CI: 0.50–0.72) with an agreement of 0.84 (95% CI: 0.80–0.89). Ten out of 36 (27.8%) patients with CAC scores above 100 did not have other cardiovascular risk factors. Conclusions Automatic CAC scoring on radiotherapy planning CT scans is a reliable method to assess CVD risk based on Agatston scores. One in four breast cancer patients planned for radiotherapy have elevated CAC score. One in three patients with high CAC scores don't have other CVD risk factors and wouldn't have been identified as high risk.
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25
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Wolterink JM, Leiner T, de Vos BD, van Hamersvelt RW, Viergever MA, Išgum I. Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks. Med Image Anal 2016; 34:123-136. [PMID: 27138584 DOI: 10.1016/j.media.2016.04.004] [Citation(s) in RCA: 160] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2016] [Revised: 04/07/2016] [Accepted: 04/19/2016] [Indexed: 02/08/2023]
Abstract
The amount of coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular events. CAC is clinically quantified in cardiac calcium scoring CT (CSCT), but it has been shown that cardiac CT angiography (CCTA) may also be used for this purpose. We present a method for automatic CAC quantification in CCTA. This method uses supervised learning to directly identify and quantify CAC without a need for coronary artery extraction commonly used in existing methods. The study included cardiac CT exams of 250 patients for whom both a CCTA and a CSCT scan were available. To restrict the volume-of-interest for analysis, a bounding box around the heart is automatically determined. The bounding box detection algorithm employs a combination of three ConvNets, where each detects the heart in a different orthogonal plane (axial, sagittal, coronal). These ConvNets were trained using 50 cardiac CT exams. In the remaining 200 exams, a reference standard for CAC was defined in CSCT and CCTA. Out of these, 100 CCTA scans were used for training, and the remaining 100 for evaluation of a voxel classification method for CAC identification. The method uses ConvPairs, pairs of convolutional neural networks (ConvNets). The first ConvNet in a pair identifies voxels likely to be CAC, thereby discarding the majority of non-CAC-like voxels such as lung and fatty tissue. The identified CAC-like voxels are further classified by the second ConvNet in the pair, which distinguishes between CAC and CAC-like negatives. Given the different task of each ConvNet, they share their architecture, but not their weights. Input patches are either 2.5D or 3D. The ConvNets are purely convolutional, i.e. no pooling layers are present and fully connected layers are implemented as convolutions, thereby allowing efficient voxel classification. The performance of individual 2.5D and 3D ConvPairs with input sizes of 15 and 25 voxels, as well as the performance of ensembles of these ConvPairs, were evaluated by a comparison with reference annotations in CCTA and CSCT. In all cases, ensembles of ConvPairs outperformed their individual members. The best performing individual ConvPair detected 72% of lesions in the test set, with on average 0.85 false positive (FP) errors per scan. The best performing ensemble combined all ConvPairs and obtained a sensitivity of 71% at 0.48 FP errors per scan. For this ensemble, agreement with the reference mass score in CSCT was excellent (ICC 0.944 [0.918-0.962]). Aditionally, based on the Agatston score in CCTA, this ensemble assigned 83% of patients to the same cardiovascular risk category as reference CSCT. In conclusion, CAC can be accurately automatically identified and quantified in CCTA using the proposed pattern recognition method. This might obviate the need to acquire a dedicated CSCT scan for CAC scoring, which is regularly acquired prior to a CCTA, and thus reduce the CT radiation dose received by patients.
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Affiliation(s)
- Jelmer M Wolterink
- Image Sciences Institute, University Medical Center Utrecht, Q.02.4.45, P.O. Box 85500, 3508 GA Utrecht, The Netherlands.
| | - Tim Leiner
- Department of Radiology, University Medical Center Utrecht, E.01.132, P.O. Box 85500, 3508 GA Utrecht, The Netherlands.
| | - Bob D de Vos
- Image Sciences Institute, University Medical Center Utrecht, Q.02.4.45, P.O. Box 85500, 3508 GA Utrecht, The Netherlands.
| | - Robbert W van Hamersvelt
- Department of Radiology, University Medical Center Utrecht, E.01.132, P.O. Box 85500, 3508 GA Utrecht, The Netherlands.
| | - Max A Viergever
- Image Sciences Institute, University Medical Center Utrecht, Q.02.4.45, P.O. Box 85500, 3508 GA Utrecht, The Netherlands.
| | - Ivana Išgum
- Image Sciences Institute, University Medical Center Utrecht, Q.02.4.45, P.O. Box 85500, 3508 GA Utrecht, The Netherlands.
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26
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Yang G, Chen Y, Ning X, Sun Q, Shu H, Coatrieux JL. Automatic coronary calcium scoring using noncontrast and contrast CT images. Med Phys 2016; 43:2174. [DOI: 10.1118/1.4945045] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
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27
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Wolterink JM, Leiner T, Takx RAP, Viergever MA, Isgum I. Automatic Coronary Calcium Scoring in Non-Contrast-Enhanced ECG-Triggered Cardiac CT With Ambiguity Detection. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:1867-78. [PMID: 25794387 DOI: 10.1109/tmi.2015.2412651] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
The amount of coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular events. We present a system that automatically quantifies total patient and per coronary artery CAC in non-contrast-enhanced, ECG-triggered cardiac CT. The system identifies candidate calcifications that cannot be automatically labeled with high certainty and optionally presents these to an expert for review. Candidates were extracted by intensity-based thresholding and described by location features derived from estimated coronary artery positions, as well as size, shape and intensity features. Next, a two-class classifier distinguished between coronary calcifications and negatives or a multiclass classifier labeled CAC per coronary artery. Candidates that could not be labeled with high certainty were identified by entropy-based ambiguity detection and presented to an expert for review and possible relabeling. The system was evaluated with 530 test images. Using the two-class classifier, the intra-class correlation coefficient (ICC) between reference and automatically determined total patient CAC volume was 0.95. Using the multiclass classifier, the ICC between reference and automatically determined per artery CAC volume was 0.98 (LAD), 0.69 (LCX), and 0.95 (RCA). In 49% of CTs, no ambiguous candidates were identified, while review of the remaining CTs increased the ICC for total patient CAC volume to 1.00, and per artery CAC volume to 1.00 (LAD), 0.95 (LCX), and 0.99 (RCA). In conclusion, CAC can be automatically identified in non-contrast-enhanced ECG-triggered cardiac CT. Ambiguity detection with expert review may enable the application of automatic CAC scoring in the clinic with a performance comparable to that of a human expert.
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Isgum I, Prokop M, Niemeijer M, Viergever MA, van Ginneken B. Automatic coronary calcium scoring in low-dose chest computed tomography. IEEE TRANSACTIONS ON MEDICAL IMAGING 2012; 31:2322-34. [PMID: 22961297 DOI: 10.1109/tmi.2012.2216889] [Citation(s) in RCA: 76] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
The calcium burden as estimated from non-ECG-synchronized computed tomography (CT) exams acquired in screening of heavy smokers has been shown to be a strong predictor of cardiovascular events. We present a method for automatic coronary calcium scoring with low-dose, non-contrast-enhanced, non-ECG-synchronized chest CT. First, a probabilistic coronary calcium map was created using multi-atlas segmentation. This map assigned an a priori probability for the presence of coronary calcifications at every location in a scan. Subsequently, a statistical pattern recognition system was designed to identify coronary calcifications by texture, size, and spatial features; the spatial features were computed using the coronary calcium map. The detected calcifications were quantified in terms of volume and Agatston score. The best results were obtained by merging the results of three different supervised classification systems, namely direct classification with a nearest neighbor classifier, and two-stage classification with nearest neighbor and support vector machine classifiers.We used a total of 231 test scans containing 45,674 mm³ of coronary calcifications. The presented method detected on average 157/198 mm³ (sensitivity 79.2%) of coronary calcium volume with on average 4 mm false positive volume. Calcium scoring can be performed automatically in low-dose, non-contrast enhanced, non-ECG-synchronized chest CT in screening of heavy smokers to identify subjects who might benefit from preventive treatment.
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Affiliation(s)
- Ivana Isgum
- Image Sciences Institute, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands.
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Bisbal J, Engelbrecht G, Villa-Uriol MC, Frangi AF. Prediction of Cerebral Aneurysm Rupture Using Hemodynamic, Morphologic and Clinical Features: A Data Mining Approach. LECTURE NOTES IN COMPUTER SCIENCE 2011. [DOI: 10.1007/978-3-642-23091-2_6] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
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