Enhancing Label-Driven Deep Deformable Image Registration with Local Distance Metrics for State-of-the-Art Cardiac Motion Tracking

  • Alessa HeringEmail author
  • Sven Kuckertz
  • Stefan Heldmann
  • Mattias P. Heinrich
Conference paper
Part of the Informatik aktuell book series (INFORMAT)


While deep learning has achieved significant advances in accuracy for medical image segmentation, its benefits for deformable image registration have so far remained limited to reduced computation times. Previous work has either focused on replacing the iterative optimization of distance and smoothness terms with CNN-layers or using supervised approaches driven by labels. Our method is the first to combine the complementary strengths of global semantic information (represented by segmentation labels) and local distance metrics that help align surrounding structures. We demonstrate significant higher Dice scores (of 86.5 %) for deformable cardiac image registration compared to classic registration (79.0 %) as well as label-driven deep learning frameworks (83.4%).


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Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019

Authors and Affiliations

  • Alessa Hering
    • 1
    • 2
    Email author
  • Sven Kuckertz
    • 1
    • 3
  • Stefan Heldmann
    • 1
  • Mattias P. Heinrich
    • 3
  1. 1.Fraunhofer MEVISLübeckDeutschland
  2. 2.Diagnostic Image Analysis GroupRadboud UMCNijmegenNiederlande
  3. 3.Institute of Medical InformaticsUniversity of LübeckLübeckDeutschland

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