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Bayesian and grAphical Models for Biomedical Imaging

International MICCAI Workshop on Medical Computer Vision

BAMBI 2016, MCV 2016: Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging pp 58–68Cite as

A Lung Graph–Model for Pulmonary Hypertension and Pulmonary Embolism Detection on DECT Images

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10081))

Abstract

This article presents a novel graph–model approach encoding the relations between the perfusion in several regions of the lung extracted from a geometry–based atlas. Unlike previous approaches that individually analyze regions of the lungs, our method evaluates the entire pulmonary circulatory network for the classification of patients with pulmonary embolism and pulmonary hypertension. An undirected weighted graph with fixed structure is used to encode the network of intensity distributions in Dual Energy Computed Tomography (DECT) images. Results show that the graph–model presented is capable of characterizing a DECT dataset of 30 patients affected with disease and 26 healthy patients, achieving a discrimination accuracy from 0.77 to 0.87 and an AUC between 0.73 and 0.86. This fully automatic graph–model of the lungs constitutes a novel and effective approach for exploring the various patterns of pulmonary perfusion of healthy and diseased patients.

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Acknowledgments

This work was partly supported by the Swiss National Science Foundation with the PH4D (320030–146804) and MAGE projects (PZ00P2_154891).

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Authors and Affiliations

  1. University of Applied Sciences Western Switzerland (HES–SO), Sierre, Switzerland

    Yashin Dicente Cid, Henning Müller & Adrien Depeursinge

  2. University of Geneva, Geneva, Switzerland

    Yashin Dicente Cid & Henning Müller

  3. Martinos Center for Biomedical Imaging, Charlestown, MA, USA

    Henning Müller

  4. University Hospitals of Geneva (HUG), Geneva, Switzerland

    Alexandra Platon, Jean–Paul Janssens, Frédéric Lador & Pierre–Alexandre Poletti

  5. École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    Adrien Depeursinge

Authors
  1. Yashin Dicente Cid
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  2. Henning Müller
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  3. Alexandra Platon
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  4. Jean–Paul Janssens
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  5. Frédéric Lador
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  6. Pierre–Alexandre Poletti
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  7. Adrien Depeursinge
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Corresponding author

Correspondence to Yashin Dicente Cid .

Editor information

Editors and Affiliations

  1. HES-SO, Sierre, Switzerland

    Henning Müller

  2. Siemens AG, Munich, Germany

    B. Michael Kelm

  3. McGill University, Montreal, Québec, Canada

    Tal Arbel

  4. University of Sydney, Sydney, New South Wales, Australia

    Weidong Cai

  5. University College London, London, United Kingdom

    M. Jorge Cardoso

  6. Medical University of Vienna, Vienna, Austria

    Georg Langs

  7. Technische Universität München, Garching, Germany

    Bjoern Menze

  8. Rutgers The State University of New Jersey, Piscataway, New Jersey, USA

    Dimitris Metaxas

  9. University of Texas Southwestern Medical Center, Dallas, Texas, USA

    Albert Montillo

  10. Brigham and Women’s Hospital, Boston, Massachusetts, USA

    William M. Wells III

  11. University of North Carolina at Charlotte, Charlotte, North Carolina, USA

    Shaoting Zhang

  12. The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong

    Albert C.S. Chung

  13. University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom

    Mark Jenkinson

  14. IcoMetrix, Leuven, Belgium

    Annemie Ribbens

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Dicente Cid, Y. et al. (2017). A Lung Graph–Model for Pulmonary Hypertension and Pulmonary Embolism Detection on DECT Images. In: Müller, H., et al. Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging. BAMBI MCV 2016 2016. Lecture Notes in Computer Science(), vol 10081. Springer, Cham. https://doi.org/10.1007/978-3-319-61188-4_6

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  • DOI: https://doi.org/10.1007/978-3-319-61188-4_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61187-7

  • Online ISBN: 978-3-319-61188-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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