Corpus ID: 57761142

Myocardial Infarction Quantification From Late Gadolinium Enhancement MRI Using Top-hat Transforms and Neural Networks

@article{Rosa2019MyocardialIQ,
  title={Myocardial Infarction Quantification From Late Gadolinium Enhancement MRI Using Top-hat Transforms and Neural Networks},
  author={Ezequiel de la Rosa and Desire Sidib{\'e} and Thomas Decourselle and Thibault Leclercq and Alexandre Cochet and Alain Lalande},
  journal={ArXiv},
  year={2019},
  volume={abs/1901.02911}
}
  • Ezequiel de la Rosa, Desire Sidibé, +3 authors Alain Lalande
  • Published in ArXiv 2019
  • Computer Science
  • Significance: Late gadolinium enhanced magnetic resonance imaging (LGE-MRI) is the gold standard technique for myocardial viability assessment. Although the technique accurately reflects the damaged tissue, there is no clinical standard for quantifying myocardial infarction (MI), demanding most algorithms to be expert dependent. Objectives and Methods: In this work a new automatic method for MI quantification from LGE-MRI is proposed. Our novel segmentation approach is devised for accurately… CONTINUE READING

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