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Linear and Deformable Image Registration with 3D Convolutional Neural Networks

  • Christodoulidis StergiosEmail author
  • Sahasrabudhe Mihir
  • Vakalopoulou Maria
  • Chassagnon Guillaume
  • Revel Marie-Pierre
  • Mougiakakou Stavroula
  • Paragios Nikos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11040)

Abstract

Image registration and in particular deformable registration methods are pillars of medical imaging. Inspired by the recent advances in deep learning, we propose in this paper, a novel convolutional neural network architecture that couples linear and deformable registration within a unified architecture endowed with near real-time performance. Our framework is modular with respect to the global transformation component, as well as with respect to the similarity function while it guarantees smooth displacement fields. We evaluate the performance of our network on the challenging problem of MRI lung registration, and demonstrate superior performance with respect to state of the art elastic registration methods. The proposed deformation (between inspiration & expiration) was considered within a clinically relevant task of interstitial lung disease (ILD) classification and showed promising results.

Keywords

Convolutional neural networks Deformable registration Unsupervised learning Lungs Breathing MRI Interstitial lung disease 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Christodoulidis Stergios
    • 1
    Email author
  • Sahasrabudhe Mihir
    • 2
  • Vakalopoulou Maria
    • 2
  • Chassagnon Guillaume
    • 2
    • 3
  • Revel Marie-Pierre
    • 3
  • Mougiakakou Stavroula
    • 1
  • Paragios Nikos
    • 4
  1. 1.ARTORG CenterUniversity of BernBernSwitzerland
  2. 2.CVN, CentraleSupélec, Université Paris-SaclayGif-sur-YvetteFrance
  3. 3.Groupe Hospitalier Cochin-Hôtel Dieu, Université Paris DescartesParisFrance
  4. 4.TheraPanaceaParisFrance

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