Combining multi-sequence and synthetic images for improved segmentation of late gadolinium enhanced MRI

  • Authors
  • Campello VM, Martín-Isla C, Izquierdo C, Petersen SE, González Ballester MA, Lekadir K
  • UPF authors
  • LEKADIR ., KARIM; CAMPELLO ROMAN, VICTOR MANUEL; GONZALEZ BALLESTER, MIGUEL ANGEL;
  • Type
  • Scholarly articles
  • Journal títle
  • Lecture Notes in Computer Science / Artificial Intelligence
  • Publication year
  • 2020
  • Volume
  • 12009
  • Pages
  • 290-299
  • ISSN
  • 0302-9743
  • Publication State
  • Published
  • Abstract
  • Accurate segmentation of the cardiac boundaries in late gadolinium enhancement magnetic resonance images (LGE-MRI) is a fundamental step for accurate quantification of scar tissue. However, while there are many solutions for automatic cardiac segmentation of cine images, the presence of scar tissue can make the correct delineation of the myocardium in LGE-MRI challenging even for human experts. As part of the Multi-Sequence Cardiac MR Segmentation Challenge, we propose a solution for LGE-MRI segmentation based on two components. First, a generative adversarial network is trained for the task of modality-to-modality translation between cine and LGE-MRI sequences to obtain extra synthetic images for both modalities. Second, a deep learning model is trained for segmentation with different combinations of original, augmented and synthetic sequences. Our results based on three magnetic resonance sequences (LGE, bSSFP and T2) from 45 different patients show that the multi-sequence model training integrating synthetic images and data augmentation improves in the segmentation over conventional training with real datasets. In conclusion, the accuracy of the segmentation of LGE-MRI images can be improved by using complementary information provided by non-contrast MRI sequences.
  • Complete citation
  • Campello VM, Martín-Isla C, Izquierdo C, Petersen SE, González Ballester MA, Lekadir K. Combining multi-sequence and synthetic images for improved segmentation of late gadolinium enhanced MRI. Lecture Notes in Computer Science / Artificial Intelligence 2020; 12009( ): 290-299.
Bibliometric indicators
  • Índex Scimago de 0.427 (2019)