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Validation of a Novel Method for the Automatic Segmentation of Left Atrial Scar from Delayed-Enhancement Magnetic Resonance

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Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges (STACOM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7085))

Abstract

Delayed-enhancement magnetic resonance imaging is an effective technique for imaging left atrial (LA) scars both pre- and post- radio-frequency ablation for the treatment of atrial fibrillation. Existing techniques for LA scar segmentation require expert manual interaction, making them tedious and prone to high observer variability. In this paper, a novel automatic segmentation algorithm for segmenting LA scar was validated using digital phantoms and clinical data from 11 patients. The performance of the approach was compared to the two leading semi-automatic techniques and the ground truth of manual segmentations by 2 expert observers. The novel approach was shown to be accurate in terms of Dice coefficient, robust to typical image intensity variability, and much faster in terms of execution time.

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Authors and Affiliations

  1. Division of Imaging Sciences and Biomedical Engineering, King’s College London, UK

    Rashed Karim, Aruna Arujuna, Alex Brazier, Jaswinder Gill, C. Aldo Rinaldi, Michael Cooklin, Mark O’Neill, Reza Razavi, Tobias Schaeffter & Kawal S. Rhode

  2. Department of Cardiology, Guy’s and St. Thomas’ Hospitals NHS Trust, London, UK

    Aruna Arujuna, Jaswinder Gill, C. Aldo Rinaldi, Michael Cooklin, Mark O’Neill & Reza Razavi

  3. Department of Computing, Imperial College London, London, UK

    Daniel Rueckert

Authors
  1. Rashed Karim
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  2. Aruna Arujuna
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  3. Alex Brazier
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  4. Jaswinder Gill
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  5. C. Aldo Rinaldi
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  6. Michael Cooklin
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  7. Mark O’Neill
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  8. Reza Razavi
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  9. Tobias Schaeffter
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  10. Daniel Rueckert
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  11. Kawal S. Rhode
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Editor information

Editors and Affiliations

  1. Department of Information and Communication Technologies, University of Pompeu Fabra, Center for ComputationalImaging and Simulation Technologies in Biomedicine (CISTIB), Plaça de la Mercè 10-12, 08002, Barcelona, Spain

    Oscar Camara

  2. Machine Learning and Perception, Microsoft Research, 7 J J Thomson Avenue, CB3 0FB, Cambridge, UK

    Ender Konukoglu

  3. Department of Medical Biophysics, Ontario Cancer Institute, princess Margaret Hospital, University of Toronto, Faculty of Medicien, 610 University Avenue, M5G 2M9, Toronto, ON, Canada

    Mihaela Pop

  4. Division of Imaging Sciences, King’s College London, St. Thomas Hospital, Lambeth Wing, SE1 7EH, London, UK

    Kawal Rhode

  5. Research Centre, Ascöepios Team, Inria Sophia Antipolis- Méditerranée, 2004 Route des Lucioles, BP 93, 06902, Sophia Antipolis Cedex, France

    Maxime Sermesant

  6. Auckland Bioengineering Institute, Medical and Health Sciences, University of Auckland, Grafton Campus, Building 502, Auckland, New Zealand

    Alistair Young

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© 2012 Springer-Verlag Berlin Heidelberg

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Karim, R. et al. (2012). Validation of a Novel Method for the Automatic Segmentation of Left Atrial Scar from Delayed-Enhancement Magnetic Resonance. In: Camara, O., Konukoglu, E., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. STACOM 2011. Lecture Notes in Computer Science, vol 7085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28326-0_26

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  • DOI: https://doi.org/10.1007/978-3-642-28326-0_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28325-3

  • Online ISBN: 978-3-642-28326-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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