An automated segmentation of coronary artery calcification using deep learning in specific region limitation

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-res...

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Bibliographic Details
Published in:Medical & biological engineering & computing 2023-07, Vol.61 (7), p.1687-1696
Main Authors: Zair, Asmae Mama, Bouzouad Cherfa, Assia, Cherfa, Yazid, Belkhamsa, Noureddine
Format: Article
Language:English
Subjects:
Algorithms
Arteries
Artificial neural networks
Automation
Bioengineering
Biomedical and Life Sciences
Biomedical Engineering and Bioengineering
Biomedicine
Calcification
Calcification (ectopic)
Cardiac muscle
Computational geometry
Computed tomography
Computer Applications
Convexity
Coronary artery
Coronary vessels
Deep learning
Disease
disease severity
Heart
Human Physiology
Image segmentation
Imaging
Lungs
Machine learning
Medical imaging
Medical research
Methods
Morphology
Muscles
myocardium
Neural networks
Original Article
prediction
Radiology
Tomography
Transfer learning
Veins & arteries
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Summary: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
ISSN:0140-0118
1741-0444
DOI:10.1007/s11517-023-02797-z