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VTrails: Inferring Vessels with Geodesic Connectivity Trees

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Information Processing in Medical Imaging (IPMI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10265))

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Abstract

The analysis of vessel morphology and connectivity has an impact on a number of cardiovascular and neurovascular applications by providing patient-specific high-level quantitative features such as spatial location, direction and scale. In this paper we present an end-to-end approach to extract an acyclic vascular tree from angiographic data by solving a connectivity-enforcing anisotropic fast marching over a voxel-wise tensor field representing the orientation of the underlying vascular tree. The method is validated using synthetic and real vascular images. We compare VTrails against classical and state-of-the-art ridge detectors for tubular structures by assessing the connectedness of the vesselness map and inspecting the synthesized tensor field as proof of concept. VTrails performance is evaluated on images with different levels of degradation: we verify that the extracted vascular network is an acyclic graph (i.e. a tree), and we report the extraction accuracy, precision and recall.

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Acknowledgements

The study is co-funded from the EPSRC grant (EP/H046410/1), the Wellcome Trust and the National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre.

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Authors and Affiliations

  1. Translational Imaging Group, CMIC, University College London, London, UK

    Stefano Moriconi, Maria A. Zuluaga, Sébastien Ourselin & M. Jorge Cardoso

  2. Institute of Neurology, University College London, London, UK

    H. Rolf Jäger & Parashkev Nachev

  3. Dementia Research Centre, University College London, London, UK

    Sébastien Ourselin & M. Jorge Cardoso

Authors
  1. Stefano Moriconi
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  2. Maria A. Zuluaga
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  3. H. Rolf Jäger
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  4. Parashkev Nachev
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  5. Sébastien Ourselin
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  6. M. Jorge Cardoso
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Corresponding author

Correspondence to Stefano Moriconi .

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Editors and Affiliations

  1. University of North Carolina, Chapel Hill, North Carolina, USA

    Marc Niethammer

  2. University of North Carolina, Chapel Hill, North Carolina, USA

    Martin Styner

  3. Kitware Inc., Carrboro, North Carolina, USA

    Stephen Aylward

  4. University of North Carolina, Chapel Hill, North Carolina, USA

    Hongtu Zhu

  5. University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Ipek Oguz

  6. University of North Carolina, Chapel Hill, North Carolina, USA

    Pew-Thian Yap

  7. University of North Carolina, Chapel Hill, North Carolina, USA

    Dinggang Shen

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Moriconi, S., Zuluaga, M.A., Jäger, H.R., Nachev, P., Ourselin, S., Cardoso, M.J. (2017). VTrails: Inferring Vessels with Geodesic Connectivity Trees. In: Niethammer, M., et al. Information Processing in Medical Imaging. IPMI 2017. Lecture Notes in Computer Science(), vol 10265. Springer, Cham. https://doi.org/10.1007/978-3-319-59050-9_53

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  • DOI: https://doi.org/10.1007/978-3-319-59050-9_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59049-3

  • Online ISBN: 978-3-319-59050-9

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