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Integrating Atlas and Graph Cut Methods for Left Ventricle Segmentation from Cardiac Cine MRI

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Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges (STACOM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10124))

Abstract

Magnetic Resonance Imaging (MRI) has evolved as a clinical standard-of-care imaging modality for cardiac morphology, function assessment, and guidance of cardiac interventions. All these applications rely on accurate extraction of the myocardial tissue and blood pool from the imaging data. Here we propose a framework for left ventricle (LV) segmentation from cardiac cine MRI. First, we segment the LV blood pool using iterative graph cuts, and subsequently use this information to segment the myocardium. We formulate the segmentation procedure as an energy minimization problem in a graph subject to the shape prior obtained by label propagation from an average atlas using affine registration. The proposed framework has been validated on 30 patient cardiac cine MRI datasets available through the STACOM LV segmentation challenge and yielded fast, robust, and accurate segmentation results.

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Notes

  1. 1.

    http://www.who.int/mediacentre/factsheets/fs317/en/.

  2. 2.

    http://www.cardiacatlas.org.

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Author information

Authors and Affiliations

  1. Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, New York, USA

    Shusil Dangi, Nathan Cahill & Cristian A. Linte

  2. Biomedical Engineering, Rochester Institute of Technology, Rochester, New York, USA

    Cristian A. Linte

  3. Center for Applied and Computational Mathematics, Rochester Institute of Technology, Rochester, New York, USA

    Nathan Cahill

Authors
  1. Shusil Dangi
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  2. Nathan Cahill
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  3. Cristian A. Linte
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Corresponding author

Correspondence to Cristian A. Linte .

Editor information

Editors and Affiliations

  1. Siemens Medical Solutions, Medical Imaging Technologies, Princeton, New Jersey, USA

    Tommaso Mansi

  2. Simula Research Laboratory, Oslo, Norway

    Kristin McLeod

  3. Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada

    Mihaela Pop

  4. Imaging Sciences and Biomedical Engineering, Rayne Institute, King’s College London, London, United Kingdom

    Kawal Rhode

  5. Inria, Asclepios Project, Sophia-Antipolis, France

    Maxime Sermesant

  6. Department of Radiology, Faculty of Medical and Health Sciences, Auckland University, Auckland, New Zealand

    Alistair Young

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Dangi, S., Cahill, N., Linte, C.A. (2017). Integrating Atlas and Graph Cut Methods for Left Ventricle Segmentation from Cardiac Cine MRI. In: Mansi, T., McLeod, K., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. STACOM 2016. Lecture Notes in Computer Science(), vol 10124. Springer, Cham. https://doi.org/10.1007/978-3-319-52718-5_9

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  • DOI: https://doi.org/10.1007/978-3-319-52718-5_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52717-8

  • Online ISBN: 978-3-319-52718-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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