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International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2012: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 pp 1–8Cite as

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Automatic Multi-model-Based Segmentation of the Left Atrium in Cardiac MRI Scans

Automatic Multi-model-Based Segmentation of the Left Atrium in Cardiac MRI Scans

  • Dominik Kutra19,
  • Axel Saalbach19,
  • Helko Lehmann19,
  • Alexandra Groth19,
  • Sebastian P. M. Dries19,
  • Martin W. Krueger20,
  • Olaf Dössel20 &
  • …
  • Jürgen Weese19 
  • Conference paper
  • 4205 Accesses

  • 7 Citations

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7511)

Abstract

Model-based segmentation approaches have been proven to produce very accurate segmentation results while simultaneously providing an anatomic labeling for the segmented structures. However, variations of the anatomy, as they are often encountered e.g. on the drainage pattern of the pulmonary veins to the left atrium, cannot be represented by a single model. Automatic model selection extends the model-based segmentation approach to handling significant variational anatomies without user interaction. Using models for the three most common anatomical variations of the left atrium, we propose a method that uses an estimation of the local fit of different models to select the best fitting model automatically. Our approach employs the support vector machine for the automatic model selection. The method was evaluated on 42 very accurate segmentations of MRI scans using three different models. The correct model was chosen in 88.1 % of the cases. In a second experiment, reflecting average segmentation results, the model corresponding to the clinical classification was automatically found in 78.0 % of the cases.

Keywords

  • Support Vector Machine
  • Pulmonary Vein
  • Left Atrium
  • Segmentation Result
  • Automatic Segmentation

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Author information

Authors and Affiliations

  1. Philips Research Laboratories, Hamburg, Germany

    Dominik Kutra, Axel Saalbach, Helko Lehmann, Alexandra Groth, Sebastian P. M. Dries & Jürgen Weese

  2. Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

    Martin W. Krueger & Olaf Dössel

Authors
  1. Dominik Kutra
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  2. Axel Saalbach
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  3. Helko Lehmann
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  4. Alexandra Groth
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  5. Sebastian P. M. Dries
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  6. Martin W. Krueger
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  7. Olaf Dössel
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  8. Jürgen Weese
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Editor information

Editors and Affiliations

  1. Project Team Asclepios, Inria Sophia Antipolis, 06902, Sophia-Antipolis, France

    Nicholas Ayache & Hervé Delingette & 

  2. MIT, CSAIL, 02139, Cambridge, MA, USA

    Polina Golland

  3. Information and Communication Headquarters, Nagoya University, 464-8603, Nagoya, Japan

    Kensaku Mori

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© 2012 Springer-Verlag Berlin Heidelberg

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Cite this paper

Kutra, D. et al. (2012). Automatic Multi-model-Based Segmentation of the Left Atrium in Cardiac MRI Scans. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7511. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33418-4_1

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  • DOI: https://doi.org/10.1007/978-3-642-33418-4_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33417-7

  • Online ISBN: 978-3-642-33418-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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