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

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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.

Deep learning for CAC segmentation in CT images

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Data availability

The authors declare that all data and materials used in this research support their published claims and comply with field standards.

Code availability

The data that support the findings of this study are available from orCaScore challenge.

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Authors and Affiliations

  1. University of Blida 01, B.P 270, Soumaa road, Blida, Algeria

    Asmae Mama Zair, Assia Bouzouad Cherfa, Yazid Cherfa & Noureddine Belkhamsa

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  1. Asmae Mama Zair
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  2. Assia Bouzouad Cherfa
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  3. Yazid Cherfa
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Contributions

AMZ did data collection. AMZ implemented the model and analyzed data. AMZ wrote the manuscript with critical input from AC, YC and NB. All authors read and approved the final manuscript.

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Correspondence to Asmae Mama Zair.

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Zair, A.M., Bouzouad Cherfa, A., Cherfa, Y. et al. An automated segmentation of coronary artery calcification using deep learning in specific region limitation. Med Biol Eng Comput 61, 1687–1696 (2023). https://doi.org/10.1007/s11517-023-02797-z

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