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Learning a Global Descriptor of Cardiac Motion from a Large Cohort of 1000+ Normal Subjects

  • Wenjia BaiEmail author
  • Devis Peressutti
  • Ozan Oktay
  • Wenzhe Shi
  • Declan P. O’Regan
  • Andrew P. King
  • Daniel Rueckert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9126)

Abstract

Motion, together with shape, reflect important aspects of cardiac function. In this work, a new method is proposed for learning of a cardiac motion descriptor from a data-driven perspective. The resulting descriptor can characterise the global motion pattern of the left ventricle with a much lower dimension than the original motion data. It has demonstrated its predictive power on two exemplar classification tasks on a large cohort of 1093 normal subjects.

Keywords

Independent Component Analysis Cardiac Motion Locally Linear Embedding Gender Classification Template Image 
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.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Wenjia Bai
    • 1
    Email author
  • Devis Peressutti
    • 2
  • Ozan Oktay
    • 1
  • Wenzhe Shi
    • 1
  • Declan P. O’Regan
    • 3
  • Andrew P. King
    • 2
  • Daniel Rueckert
    • 1
  1. 1.Biomedical Image Analysis Group, Department of ComputingImperial College LondonLondonUK
  2. 2.Division of Imaging Sciences and Biomedical EngineeringKing’s College LondonLondonUK
  3. 3.MRC Clinical Sciences Centre, Hammersmith HospitalImperial College LondonLondonUK

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