Abstract: mlVIRNET

Improved Deep Learning Registration Using a Coarse to Fine Approach to Capture all Levels of Motion
  • Alessa HeringEmail author
  • Stefan Heldmann
Conference paper
Part of the Informatik aktuell book series (INFORMAT)


While deep learning has become a methodology of choice in many areas, relatively few deep-learning-based image registration algorithms have been proposed. One reason for this is lack of ground-truth and the large variability of plausible deformations that can align corresponding anatomies. Therefore, the problem is much less constrained than for example image classification or segmentation.


  1. 1.
    Hering A, van Ginneken B, Heldmann S. MlVIRNET: multilevel variational image registration network. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; 2019. p. 257–265.Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2020

Authors and Affiliations

  1. 1.Fraunhofer MEVISLübeckDeutschland
  2. 2.Diagnostic Image Analysis GroupRadboudumcNijmegenNiederlande

Personalised recommendations