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Automated Model-Based Left Ventricle Segmentation in Cardiac MR Images

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Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges (STACOM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9534))

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Abstract

We present a fully automated system for segmenting the Left Ventricle (LV) in cardiac MR images based on statistical and deformable models. A Project-Out Inverse Compositional Active Appearance Model of 3D LV shape produces segmentations that are refined using a unified statistical/deterministic deformable model. A new multi-scale detector, based on the Histogram of Oriented Gradients (HoG), produces initial estimates of LV position and scale in the MR volume. The performance of the HoG detector and the deformable-model-based segmentation components are evaluated on the 30 MICCAI Grand Challenge test images. The average F-measure for detector bounding box overlap is 0.89. The average F-measures for contour overlap are 0.80 (endo), 0.82 (epi), and 0.46 (myocardium).

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Authors and Affiliations

  1. Computer Science Department, University of California, Los Angeles, USA

    Sharath Gopal & Demetri Terzopoulos

Authors
  1. Sharath Gopal
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  2. Demetri Terzopoulos
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Corresponding author

Correspondence to Sharath Gopal .

Editor information

Editors and Affiliations

  1. Universitat Pompeu Fabra, Barcelona, Spain

    Oscar Camara

  2. Siemens Corporation, Princeton, New Jersey, USA

    Tommaso Mansi

  3. University of Toronto, Toronto, Canada

    Mihaela Pop

  4. King's College, London, United Kingdom

    Kawal Rhode

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

    Maxime Sermesant

  6. Fac. of Medical and Health Sciences, Auckland Univ, Dept. Radiology, Auckland, New Zealand

    Alistair Young

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Gopal, S., Terzopoulos, D. (2016). Automated Model-Based Left Ventricle Segmentation in Cardiac MR Images. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. STACOM 2015. Lecture Notes in Computer Science(), vol 9534. Springer, Cham. https://doi.org/10.1007/978-3-319-28712-6_1

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  • DOI: https://doi.org/10.1007/978-3-319-28712-6_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28711-9

  • Online ISBN: 978-3-319-28712-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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