International Workshop on Statistical Atlases and Computational Models of the Heart

Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges pp 180-189 | Cite as

Myocardial Infarction Detection from Left Ventricular Shapes Using a Random Forest

  • Jack Allen
  • Ernesto Zacur
  • Erica Dall’Armellina
  • Pablo Lamata
  • Vicente Grau
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9534)

Abstract

Understanding myocardial remodelling, and developing tools for its accurate quantification, is fundamental for improving the diagnosis and treatment of myocardial infarction patients. Conventional clinical metrics, such as blood pool volume or ejection fraction, are not always distinctive. Here we describe a method for the classification of myocardial infarction from 3D diastolic and systolic left ventricle shapes, represented by point sets. Classification features included global geometric, shape and thickness descriptors, and a random forest was used for classification. Results from cross validation show an accuracy of 92.5 % (leave-one-out) and 91.5 % (5-fold), improving the 87 % obtained with ejection fraction thresholds. These results suggest that refined remodelling metrics provide information beyond standard clinical descriptors.

Keywords

Statistical shape model Random forest Left ventricle Myocardial infarction 

Notes

Acknowledgements

This work was supported by funding from the Medical Research Council (MRC) and Engineering and Physical Sciences Research Council (EPSRC) [grant number EP/L016052/1].

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jack Allen
    • 1
  • Ernesto Zacur
    • 2
  • Erica Dall’Armellina
    • 1
  • Pablo Lamata
    • 2
  • Vicente Grau
    • 1
  1. 1.University of OxfordOxfordUK
  2. 2.King’s College LondonLondonUK

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