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International Workshop on Reconstruction and Analysis of Moving Body Organs

International Workshop on Whole-Heart and Great Vessel Segmentation from 3D Cardiovascular MRI in Congenital Heart Disease

RAMBO 2016, HVSMR 2016: Reconstruction, Segmentation, and Analysis of Medical Images pp 159–164Cite as

Automatic Heart and Vessel Segmentation Using Random Forests and a Local Phase Guided Level Set Method

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10129))

Abstract

In this report, a novel automatic heart and vessel segmentation method is proposed. The heart segmentation pipeline consists of three major steps: heart localization using landmark detection, heart isolation using statistical shape model and myocardium segmentation using learning based voxel classification and local phase analysis. In our preliminary test, the proposed method achieved encouraging results.

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References

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Author information

Authors and Affiliations

  1. School of Technology and Health (STH), KTH Royal Institute of Technology, Stockholm, Sweden

    Chunliang Wang & Örjan Smedby

  2. School of Biomedical Engineering, Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China

    Qian Wang

Authors
  1. Chunliang Wang
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  2. Qian Wang
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  3. Örjan Smedby
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Corresponding author

Correspondence to Chunliang Wang .

Editor information

Editors and Affiliations

  1. University College London, London, United Kingdom

    Maria A. Zuluaga

  2. King’s College London, London, United Kingdom

    Kanwal Bhatia

  3. Imperial College London, London, United Kingdom

    Bernhard Kainz

  4. Harvard Medical School and Boston Children’s Hospital, Boston, Massachusetts, USA

    Mehdi H. Moghari

  5. MIT, Cambridge, Massachusetts, USA

    Danielle F. Pace

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© 2017 Springer International Publishing AG

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Wang, C., Wang, Q., Smedby, Ö. (2017). Automatic Heart and Vessel Segmentation Using Random Forests and a Local Phase Guided Level Set Method. In: Zuluaga, M., Bhatia, K., Kainz, B., Moghari, M., Pace, D. (eds) Reconstruction, Segmentation, and Analysis of Medical Images. RAMBO HVSMR 2016 2016. Lecture Notes in Computer Science(), vol 10129. Springer, Cham. https://doi.org/10.1007/978-3-319-52280-7_16

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  • DOI: https://doi.org/10.1007/978-3-319-52280-7_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52279-1

  • Online ISBN: 978-3-319-52280-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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