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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 58–65Cite as

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A Dynamical Appearance Model Based on Multiscale Sparse Representation: Segmentation of the Left Ventricle from 4D Echocardiography

A Dynamical Appearance Model Based on Multiscale Sparse Representation: Segmentation of the Left Ventricle from 4D Echocardiography

  • Xiaojie Huang19,
  • Donald P. Dione22,
  • Colin B. Compas20,
  • Xenophon Papademetris20,21,
  • Ben A. Lin22,
  • Albert J. Sinusas21,22 &
  • …
  • James S. Duncan19,20,21 
  • Conference paper
  • 4392 Accesses

  • 9 Citations

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7512)

Abstract

The spatio-temporal coherence in data plays an important role in echocardiographic segmentation. While learning offline dynamical priors from databases has received considerable attention, these priors may not be suitable for post-infarct patients and children with congenital heart disease. This paper presents a dynamical appearance model (DAM) driven by individual inherent data coherence. It employs multiscale sparse representation of local appearance, learns online multiscale appearance dictionaries as the image sequence is segmented sequentially, and integrates a spectrum of complementary multiscale appearance information including intensity, multiscale local appearance, and dynamical shape predictions. It overcomes the limitations of database-driven statistical models and applies to a broader range of subjects. Results on 26 4D canine echocardiographic images acquired from both healthy and post-infarct subjects show that our method significantly improves segmentation accuracy and robustness compared to a conventional intensity model and our previous single-scale sparse representation method.

Keywords

  • Sparse Representation
  • Segmentation Accuracy
  • Dictionary Learning
  • Dynamical Prior
  • Epicardial Border

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.

This work was supported by NIH RO1HL082640.

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Author information

Authors and Affiliations

  1. Departments of 1 Electrical Engineering, Yale University, New Haven, CT, USA

    Xiaojie Huang & James S. Duncan

  2. Biomedical Engineering, Yale University, New Haven, CT, USA

    Colin B. Compas, Xenophon Papademetris & James S. Duncan

  3. Diagnostic Radiology, Yale University, New Haven, CT, USA

    Xenophon Papademetris, Albert J. Sinusas & James S. Duncan

  4. Internal Medicine, Yale University, New Haven, CT, USA

    Donald P. Dione, Ben A. Lin & Albert J. Sinusas

Authors
  1. Xiaojie Huang
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  2. Donald P. Dione
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  3. Colin B. Compas
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  4. Xenophon Papademetris
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  5. Ben A. Lin
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  6. Albert J. Sinusas
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  7. James S. Duncan
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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

Huang, X. et al. (2012). A Dynamical Appearance Model Based on Multiscale Sparse Representation: Segmentation of the Left Ventricle from 4D Echocardiography. 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 7512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33454-2_8

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  • DOI: https://doi.org/10.1007/978-3-642-33454-2_8

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