The DOI matches its Crossref record; 59 of the 64 references it cites were found in a registry or an index.
Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks
Open accessAbstract
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 b…
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“Although other studies have investigated deep learning algorithms for automated coronary calcium quantification34–42, they used smaller cohorts or proprietary technologies.”
From Deep convolutional neural networks to predict cardiovascular risk from computed tomography (2021) · cites this paper as [34]
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Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
(2014) · Cited by 33,419
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1993.341010 · Cited by 14,475
(2010) · Cited by 14,134
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
Quantification of coronary artery calcium using ultrafast computed tomography
Agatston, A. S., Janowitz, W. R., Hildner, F. J., Zusmer, N. R., Viamonte, M., & Detrano, R. (1990) · Journal of the American College of Cardiology · doi:10.1016/0735-1097(90)90282-t · Cited by 7,786
(2011) · Cited by 6,993
Medical image analysis: progress over two decades and the challenges ahead
Duncan, J. S., & Ayache, N. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.824822 · Cited by 4,167
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Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759
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Metadata
- Authors
- Wolterink, J. M., Leiner, T., de Vos, B. D., van Hamersvelt, R. W., Viergever, M. A., & Išgum, I.
- Affiliations
- University Medical Center Utrecht (NL)
- Corresponding author
- Wolterink, Jelmer M.
- ORCID
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2016-12
- Volume
- 34
- Pages
- 123-136
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W2345003174
- Cited by
- 321
- References
- 64
- Citations by year
- 2026: 16 · 2025: 23 · 2024: 36 · 2023: 34 · 2022: 40 · 2021: 41 · 2020: 45 · 2019: 38 · 2018: 28 · 2017: 17 · 2016: 3
- FWCI
- 24.89
- Type
- journal article
- Language
- en
- Open access
- green
- dspace.library.uu.nl
- Licence
- Locations
- Medical Image Analysis (publishedVersion)
- PubMed (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Pure Amsterdam UMC (submittedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion)
- Funders
- Nvidia
- ZonMw
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- coronary artery calcification, cardiac CT angiography, coronary artery calcium scoring, voxel classification, ensemble learning, cardiovascular risk prediction, coronary artery disease screening, false positive reduction
- MeSH terms
- Computed Tomography Angiography, Algorithms, Calcium, Coronary Artery Disease, Humans, Risk Factors, Sensitivity and Specificity, Reproducibility of Results, Neural Networks, Computer
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