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Cited by 263Cardiac Imaging and DiagnosticsCOVID-19 diagnosis using AIRadiomics and Machine Learning in Medical Imaging

Automatic Calcium Scoring in Low-Dose Chest CT Using Deep Neural Networks With Dilated Convolutions

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Lessmann, Nikolas, van Ginneken, Bram, Zreik, Majd, de Jong, Pim A., de Vos, Bob D., Viergever, Max A., Isgum, Ivana·2018·IEEE Transactions on Medical Imaging
DOI 10.1109/tmi.2017.2769839
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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 F1scores 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 F1scores 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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  • Other studies that investigated deep learning algorithms for automated coronary calcium quantification used smaller cohorts or proprietary technologies.

    “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 [37]

    The abstract (p. 1) confirms that this paper studied automated coronary calcium scoring with convolutional neural networks, and the Dataset section (p. 2) reports 1,744 selected scans. Our copy does not establish that this cohort was smaller than the citing study’s or that the technology was proprietary; the Introduction (p. 2) instead describes this dataset as large relative to most earlier methods. (LLM, conf=0.78)

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Authors
Lessmann, N., van Ginneken, B., Zreik, M., de Jong, P. A., de Vos, B. D., Viergever, M. A., & Isgum, I.
Affiliations
  • Utrecht University (NL)
  • Department of Radiology and Nuclear Medicine, Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, GA, The Netherlands
  • Department of Radiology, University Medical Center Utrecht, Utrecht University, CX, The Netherlands
  • Image Sciences Institute, University Medical Center Utrecht, Utrecht University, The Netherlands
ORCID
Venue
IEEE Transactions on Medical Imaging
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2018-02
Volume
37
Issue
2
Pages
615-625
ISSN
  • 0278-0062 (ISSN-L)
  • 1558-254X
DOI
10.1109/tmi.2017.2769839
OpenAlex
W2765669952
Cited by
263
References
5
Citations by year
2026: 12 · 2025: 21 · 2024: 34 · 2023: 24 · 2022: 39 · 2021: 53 · 2020: 32 · 2019: 36 · 2018: 12
FWCI
16.79
Type
journal article
Language
en
Open access
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations
  • IEEE Transactions on Medical Imaging (publishedVersion)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Data Archiving and Networked Services (DANS) (submittedVersion)
  • Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
  • Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion)
  • Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
  • Pure Amsterdam UMC (submittedVersion, other-oa)
  • Radboud Repository (Radboud University) (submittedVersion)
  • Radboud Repository (Radboud University) (submittedVersion, other-oa)
  • arXiv (Cornell University)
Funders
National Cancer Institute
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords
coronary artery calcification, aortic valve calcification, mitral valve calcification, low-dose chest CT, cardiovascular risk assessment, atherosclerotic calcification, deep learning, dilated convolutional neural network, National Lung Screening Trial, lung cancer screening
MeSH terms
Aged, Algorithms, Aortic Valve, Aortic Valve Stenosis, Calcinosis, Coronary Artery Disease, Coronary Vessels, Humans, Lung Neoplasms, Middle Aged, Radiography, Thoracic, Tomography, X-Ray Computed, Neural Networks, Computer

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