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

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Machine Learning in Medical Imaging (MLMI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9352))

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Abstract

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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Author information

Authors and Affiliations

  1. Institute for Infocomm Research, A*STAR, 1 Fusionopolis Way, #21-01 Connexis, Singapore, 138632, Singapore

    Yanling Chi, Weimin Huang & Jiayin Zhou

  2. National Heart Center Singapore, 5 Hospital Drive, Singapore, 169609, Singapore

    Liang Zhong, Swee Yaw Tan, Keng Yung Jih Felix & Ru San Tan

  3. Department of Diagnostic Radiology, Singapore General Hospital, Singapore, 169608, Singapore

    Low Choon Seng Sheon

Authors
  1. Yanling Chi
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  2. Weimin Huang
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  3. Jiayin Zhou
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  4. Liang Zhong
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  5. Swee Yaw Tan
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  6. Keng Yung Jih Felix
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  7. Low Choon Seng Sheon
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  8. Ru San Tan
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Corresponding author

Correspondence to Yanling Chi .

Editor information

Editors and Affiliations

  1. School of Computing and Information Technology, University of Wollongong, Wollongong, NSW, Australia

    Luping Zhou

  2. Radiology and BRIC, University of North Carolina, Chapel Hill, NC, USA

    Li Wang

  3. Biomedical Engineering, Shanghai Jiaotong University, Shanghai, China

    Qian Wang

  4. Computer Science, Nanjing University, Nanjing, China

    Yinghuan Shi

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Chi, Y. et al. (2015). A Composite of Features for Learning-Based Coronary Artery Segmentation on Cardiac CT Angiography. In: Zhou, L., Wang, L., Wang, Q., Shi, Y. (eds) Machine Learning in Medical Imaging. MLMI 2015. Lecture Notes in Computer Science(), vol 9352. Springer, Cham. https://doi.org/10.1007/978-3-319-24888-2_33

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  • DOI: https://doi.org/10.1007/978-3-319-24888-2_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24887-5

  • Online ISBN: 978-3-319-24888-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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