A Composite of Features for Learning-Based Coronary Artery Segmentation on Cardiac CT Angiography

  • Yanling ChiEmail author
  • Weimin Huang
  • Jiayin Zhou
  • Liang Zhong
  • Swee Yaw Tan
  • Keng Yung Jih Felix
  • Low Choon Seng Sheon
  • Ru San Tan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9352)


Coronary artery segmentation is important in quantitative coronary angiography. In this work, a novel method is proposed for coronary artery segmentation. It integrates coronary artery features of density, local shape and global structure into the learning framework. The density feature is the vessel’s relative density estimated by means of Gaussian mixture models and is able to suppress individual variances. The local tube shape of the vessel is measured with the advantages of the 3-dimensional multi-scale Hessian filter and is able to enhance the small vessels. The global structure feature is predicted from a support vector regression in terms of vessel’s spatial position and emphasizes the geometric morphometric attribute of the coronary artery tree running across the surface of the heart. The features are fed into a support vector classifier for vessel segmentation. The proposed methodology was tested on ten 3D cardiac computed tomography angiography datasets. It obtained a sensitivity of 81%, a specificity of 99%, and Dice coefficient of 84%. The performance is good.


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Authors and Affiliations

  • Yanling Chi
    • 1
    Email author
  • Weimin Huang
    • 1
  • Jiayin Zhou
    • 1
  • Liang Zhong
    • 2
  • Swee Yaw Tan
    • 2
  • Keng Yung Jih Felix
    • 2
  • Low Choon Seng Sheon
    • 3
  • Ru San Tan
    • 2
  1. 1.Institute for Infocomm Research, A*STARSingaporeSingapore
  2. 2.National Heart Center SingaporeSingaporeSingapore
  3. 3.Department of Diagnostic RadiologySingapore General HospitalSingaporeSingapore

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