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An Automated System for 3D Segmentation of CT Angiograms

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Visual Computing

Part of the book series: Augmented Vision and Reality ((Augment Vis Real,volume 4))

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Abstract

This chapter presents a novel automated two-step algorithm for segmentation of the entire arterial tree in 3D contrast-enhanced Computed Tomography Angiography (CTA) datasets. In the first stage of the proposed algorithm, the main branches of the coronary arteries are extracted from the volume datasets based on a generalised active contour model by utilising both local and global intensity features. The use of local regional information allows for accommodating uneven brightness distribution across the image. The global energy term, derived from the histogram distribution of the input images, is used to deform the contour towards to desired boundaries without being trapped in local stationary points. Possible outliers, such as kissing vessel artefacts, are removed in the following stage by the proposed slice-by-slice correction algorithm. Experimental results on real clinical datasets have shown that our method is able to extract the major branches of the coronaries with an average distance of 0.7 voxels to the manually defined reference data. Furthermore, in the presence of kissing vessel artefacts, the outer surface of the coronary tree extracted by the proposed system is smooth and contains less erroneous segmentation as compared to the initial segmentation.

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Abbreviations

CTA:

Computed tomography angiography

CAD:

Coronary artery disease

CT:

Computed tomography

EM:

Expectation maximisation

GMM:

Gaussian mixture model

LAD:

Left anterior descending

LCA:

Left coronary artery

LCX:

Left circumflex

LM:

Left main coronary

RCA:

Right coronary artery

WHO:

World Health Organization

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Acknowledgments

The authors would like to acknowledge the support of City University, which enabled this research through the award of a University Research Studentship and Dr Gerry Carr-White and Rebecca Preston at St Thomas and Guys Hospitals for their invaluable advice and the provision of the CTA datasets.

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Authors and Affiliations

  1. College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Y. Wang

  2. Department of Electrical and Electronic Engineering, City University London, London, United Kingdom

    P. Liatsis

Authors
  1. Y. Wang
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  2. P. Liatsis
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Correspondence to P. Liatsis .

Editor information

Editors and Affiliations

  1. Mechanical Engineering Department, Universidade Federal Fluminense, Niterói – Rio de Janeiro, Brazil

    Fabiana Rodrigues Leta

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Wang, Y., Liatsis, P. (2014). An Automated System for 3D Segmentation of CT Angiograms. In: Rodrigues Leta, F. (eds) Visual Computing. Augmented Vision and Reality, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55131-4_5

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  • DOI: https://doi.org/10.1007/978-3-642-55131-4_5

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-55130-7

  • Online ISBN: 978-3-642-55131-4

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