Automatic Multi-Atlas Segmentation of Myocardium with SVF-Net

  • Marc-Michel Rohé
  • Maxime Sermesant
  • Xavier Pennec
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10663)


Segmentation of the myocardium is a key step for image guided diagnosis in many cardiac diseases. In this article, we propose an automatic multi-atlas segmentation framework which relies on a very fast registration algorithm trained with convolutional neural networks. The speed of this registration method allows us to use a high number of templates in the multi-atlas segmentation while remaining computationally tractable. The performance of the propose approach is evaluated on a dataset of 100 end-diastolic and end-systolic MRI images of the STACOM 2017 Automated Cardiac Diagnosis Challenge (ACDC).


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Marc-Michel Rohé
    • 1
  • Maxime Sermesant
    • 1
  • Xavier Pennec
    • 1
  1. 1.Université Côte d’Azur, InriaSophia-AntipolisFrance

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