ArticleInternational journal of computer assisted radiology and surgery2014
Fully automatic model-based calcium segmentation and scoring in coronary CT angiography.
Article in International journal of computer assisted radiology and surgery, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.Academic radiology · 2019Pooled it
- Automated quantification of abdominal aortic calcification using 3D nnU-Net: a novel approach to assess AAA rupture risk.BMC medical imaging · 2025Article
- A fully automated deep learning approach for coronary artery segmentation and comprehensive characterization.APL bioengineering · 2024Article
- Deep Learning-Based Automated Quantification of Coronary Artery Calcification for Contrast-Enhanced Coronary Computed Tomographic Angiography.Journal of cardiovascular development and disease · 2023Article
- Evaluation of an artificial intelligence coronary artery calcium scoring model from computed tomography.European radiology · 2023Article
- Machine Learning and Coronary Artery Calcium Scoring.Current cardiology reports · 2020Review
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Authors and funding
2 authors.
Funding
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Abstract
purposeThe paper presents new methods for automatic coronary calcium detection, segmentation and scoring in coronary CT angiography (cCTA) studies.
methodsCalcium detection and segmentation are performed by modeling image intensity profiles of coronary arteries. The scoring algorithm is based on a simulated unenhanced calcium score (CS) CT image, constructed by virtually removing the contrast media from cCTA. The methods are implemented as part of a fully automatic system for CS assessment from cCTA.
resultsThe system was tested in two independent clinical trials on 263 studies and demonstrated 0.95/0.91 correlation between the CS computed from cCTA and the standard Agatston score derived from unenhanced CS CT. The mean absolute percent difference (MAPD) of 36/39 % between the two scores lies within the error range of the standard CS CT (15-65 %).
conclusionsHigh diagnostic performance, combined with the benefits of the fully automatic solution, suggests that the proposed technique can be used to eliminate the need in a separate CS CT scan as part of the cCTA examination, thus reducing the radiation exposure and simplifying the procedure.
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