Estimation of Healthy and Fibrotic Tissue Distributions in DE-CMR Incorporating CINE-CMR in an EM Algorithm

  • Susana Merino-Caviedes
  • Lucilio Cordero-Grande
  • M. Teresa Sevilla-Ruiz
  • Ana Revilla-Orodea
  • M. Teresa Pérez Rodríguez
  • César Palencia de Lara
  • Marcos Martín-Fernández
  • Carlos Alberola-López
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10663)


Delayed Enhancement (DE) Cardiac Magnetic Resonance (CMR) allows practitioners to identify fibrosis in the myocardium. It is of importance in the differential diagnosis and therapy selection in Hypertrophic Cardiomyopathy (HCM). However, most clinical semiautomatic scar quantification methods present high intra- and interobserver variability in the case of HCM. Automatic methods relying on mixture model estimation of the myocardial intensity distribution are also subject to variability due to inaccuracies of the myocardial mask. In this paper, the CINE-CMR image information is incorporated to the estimation of the DE-CMR tissue distributions, without assuming perfect alignment between the two modalities nor the same label partitions in them. For this purpose, we propose an expectation maximization algorithm that estimates the DE-CMR distribution parameters, as well as the conditional probabilities of the DE-CMR labels with respect to the labels of CINE-CMR, with the latter being an input of the algorithm. Our results show that, compared to applying the EM using only the DE-CMR data, the proposed algorithm is more accurate in estimating the myocardial tissue parameters and obtains higher likelihood of the fibrosis voxels, as well as a higher Dice coefficient of the subsequent segmentations.


Scar segmentation EM algorithm Hypertrophic cardiomyopathy 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Laboratorio de Procesado de ImagenUniversidad de ValladolidValladolidSpain
  2. 2.Department of Biomedical EngineeringKing’s College LondonLondonUK
  3. 3.Unidad de Imagen CardiacaHospital Clínico Universitario de Valladolid, CIBER de Enfermedades Cardiovasculares (CIBERCV)ValladolidSpain
  4. 4.Dpto. de Matemática AplicadaUniversidad de ValladolidValladolidSpain

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