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mlVIRNET: Multilevel Variational Image Registration Network

  • Alessa HeringEmail author
  • Bram van Ginneken
  • Stefan Heldmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)

Abstract

We present a novel multilevel approach for deep learning based image registration. Recently published deep learning based registration methods have shown promising results for a wide range of tasks. However, these algorithms are still limited to relatively small deformations. Our method addresses this shortcoming by introducing a multilevel framework, which computes deformation fields on different scales, similar to conventional methods. Thereby, a coarse-level alignment is obtained first, which is subsequently improved on finer levels. We demonstrate our method on the complex task of inhale-to-exhale lung registration. We show that the use of a deep learning multilevel approach leads to significantly better registration results.

Keywords

Image registration Multilevel Deep learning Thoracic CT 

Notes

Acknowledgements

We gratefully acknowledge the COPDGene Study for providing the data used. COPDGene is funded by Award Number U01 HL089897 and Award Number U01 HL089856 from the National Heart, Lung, and Blood Institute. The COPDGene project is also supported by the COPD Foundation through contributions made to an Industry Advisory Board comprised of AstraZeneca, Boehringer Ingelheim, GlaxoSmithKline, Novartis, Pfizer, Siemens and Sunovion.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alessa Hering
    • 1
    • 2
    Email author
  • Bram van Ginneken
    • 2
  • Stefan Heldmann
    • 1
  1. 1.Fraunhofer MEVISLübeckGermany
  2. 2.Diagnostic Image Analyse GroupRadboudumcNijmegenThe Netherlands

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