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Joint Clustering and Component Analysis of Spatio-Temporal Shape Patterns in Myocardial Infarction

  • Catarina PintoEmail author
  • Serkan Çimen
  • Ali Gooya
  • Karim Lekadir
  • Alejandro F. Frangi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9534)

Abstract

The Left Ventricle (LV) undergoes remodelling after Myocardial Infarction (MI). In order to quantify the remodelling status, clinicians make use of conventional measures, not fully exploiting the available shape information. To characterize the changes in heart shape and classify heart data as normal or infarcted, we use a hierarchical generative model, which jointly clusters shape point sets from LV in End-Systolic (ED) and End-Systolic (ES) phases, and estimates the probability density function (pdf) of each cluster. We use a Variational Bayes (VB) method to infer the clusters labels, the mean models, and variation modes for the clusters. We also present the results in the supervised setting, where the labels of training data sets are given. Our classification results are evaluated in terms of sensitivity, specificity, and accuracy using 200 LV shapes provided by MICCAI 2015 STACOM LV Statistical Shape Modelling Challenge. Our method successfully classifies the data, achieving a specificity of 0.92 \(\pm \) 0.06 and a sensitivity of 0.96 \(\pm \) 0.07 for the supervised learning approach, and a specificity of 0.83 \(\pm \) 0.03 and a sensitivity of 0.97 \(\pm \) 0.01 for the unsupervised learning approach.

Keywords

Left Ventricle Gaussian Mixture Model Unsupervised Learning Statistical Shape Model Unsupervised Approach 
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 2016

Authors and Affiliations

  • Catarina Pinto
    • 1
    Email author
  • Serkan Çimen
    • 1
  • Ali Gooya
    • 1
  • Karim Lekadir
    • 2
  • Alejandro F. Frangi
    • 1
  1. 1.Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB)University of SheffieldSheffieldUK
  2. 2.CISTIB, Universitat Pompeu FabraBarcelonaSpain

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