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Fast Fully-Automatic Cardiac Segmentation in MRI Using MRF Model Optimization, Substructures Tracking and B-Spline Smoothing

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Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges (STACOM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10663))

Abstract

We present a fast fully automatic method for cardiac segmentation in ED and ES short axis MRI. At first we extract a region where the whole heart is situated, using a new, time-based approach. Then, the segmentation in LV, myocardium and right ventricle (RV) is obtained for a slice in a basal ED slice where both cavities are well distinguished. The extracted regions are tracked for the whole slice sequence backwards and forwards in ED. In all cases the segmentation is based on MRF optimization in four classes, two for the blood areas, and one for the myocardium and the background. Subsequently the segmentation in the ES images is based on the result of ED segmentation. As the epicardium is not well delineated, a smoothing process based on spline curves is used for obtaining the final result. We consider that, with an unsupervised method, we have obtained good results for LV and satisfactory for the RV and the myocardium on the ACDC 2017 datasets.

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Authors and Affiliations

  1. Department of Computer Science, University of Crete, Heraklion, Greece

    Elias Grinias & Georgios Tziritas

Authors
  1. Elias Grinias
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  2. Georgios Tziritas
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Corresponding author

Correspondence to Georgios Tziritas .

Editor information

Editors and Affiliations

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

    Mihaela Pop

  2. Inria-Asclepios, Sophia Antipolis, France

    Maxime Sermesant

  3. Université de Sherbrooke, Quebec City, Québec, Canada

    Pierre-Marc Jodoin

  4. University Bourgogne, Dijon, France

    Alain Lalande

  5. Fudan University, Shanghai, China

    Xiahai Zhuang

  6. Cardiovascular Research Center, National Heart and Lung Institute, Royal Brompton Hospital, London, United Kingdom

    Guang Yang

  7. University of Auckland, Auckland, New Zealand

    Alistair Young

  8. CREATIS, INSA-Lyon, Villeurbanne, France

    Olivier Bernard

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Grinias, E., Tziritas, G. (2018). Fast Fully-Automatic Cardiac Segmentation in MRI Using MRF Model Optimization, Substructures Tracking and B-Spline Smoothing. In: Pop, M., et al. Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges. STACOM 2017. Lecture Notes in Computer Science(), vol 10663. Springer, Cham. https://doi.org/10.1007/978-3-319-75541-0_10

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  • DOI: https://doi.org/10.1007/978-3-319-75541-0_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75540-3

  • Online ISBN: 978-3-319-75541-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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