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Prediction of Clinical Information from Cardiac MRI Using Manifold Learning

  • Haiyan Wang
  • Wenzhe Shi
  • Wenjia Bai
  • Antonio M. Simoes Monteiro de Marvao
  • Timothy J. W. Dawes
  • Declan P. O’Regan
  • Philip Edwards
  • Stuart Cook
  • Daniel RueckertEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9126)

Abstract

Cardiac MR imaging contains rich information that can be used to investigate the anatomy and function of the heart. In this paper, we demonstrate that it is possible to learn anatomical and functional information from cardiac MR imaging without explicit segmentation in order to predict clinical variables such as blood pressure with high accuracy. To learn the anatomical variations, we build manifolds of different time points across different subjects. In addition, we investigate two different approaches to incorporate motion information into a manifold, and compare these manifolds to a manifold learned from a single time point. Combining both inter- and intra-subject variation, we are able to construct accurate and reliable classifiers to predict clinical variables. Our proposed method does not require any explicit image segmentation and motion estimation and is able to predict clinical variables with good accuracy.

Keywords

Neighbourhood Size Local Linear Embedding Manifold Learning Manifold Model Gaussian Radial Basis Function Kernel 
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

  • Haiyan Wang
    • 1
  • Wenzhe Shi
    • 1
  • Wenjia Bai
    • 1
  • Antonio M. Simoes Monteiro de Marvao
    • 2
  • Timothy J. W. Dawes
    • 2
  • Declan P. O’Regan
    • 2
  • Philip Edwards
    • 1
  • Stuart Cook
    • 2
  • Daniel Rueckert
    • 1
    Email author
  1. 1.Biomedical Image Analysis GroupImperial College LondonLondonUK
  2. 2.Hammersmith HospitalImperial College LondonLondonUK

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