Strain-Based Parameters for Infarct Localization: Evaluation via a Learning Algorithm on a Synthetic Database of Pathological Hearts

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10263)


Localization of infarcted regions is essential to determine the most appropriate treatment for patients with cardiac ischemia. Myocardial strain partially reflects the location of infarcted regions, which demonstrated potential use in clinical practice. However, strain patterns are complex and simple thresholding is not sufficient to locate the infarcts. Besides, many strain-based parameters exist and their sensitivities to myocardial infarcts have not been directly investigated. In our study, we propose to evaluate nine strain-based parameters to locate infarcted regions. For this purpose, we designed a large database (n = 200) of synthetic pathological finite-element heart models from 5 real healthy left ventricle geometries. The infarcts were incorporated with random location, shape and degree of severity. In addition, we used a state-of-the-art learning algorithm to link deformation patterns and infarct location. Based on our evaluation, we propose to sort the strain-based parameters into three groups according to their performances in locating infarcts.


Finite-element model Myocardial infarct Myocardial strain Infarct diagnosis Machine learning 



GK Rumindo is supported by the European Commission H2020 Marie Sklodowska-Curie European Training Network VPH-CaSE (, grant agreement No 642612. This work was performed within the LABEX PRIMES (ANR-11-LABX-0063) of Université de Lyon, within the program “Investissements d’Avenir” (ANR-11-IDEX-0007) operated by the French National Research Agency (ANR); and the IMPULSION project from the Programme Avenir Lyon - St. Etienne.


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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Univ.Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206LyonFrance
  2. 2.University of Savoie Mont Blanc, Polytech Annecy-Chambéry, Laboratory TIMC-IMAG/DyCTiM2, UGA, CNRSGrenobleFrance

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