Adversarial Image Registration with Application for MR and TRUS Image Fusion

  • Pingkun YanEmail author
  • Sheng Xu
  • Ardeshir R. Rastinehad
  • Brad J. Wood
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11046)


Robust and accurate alignment of multimodal medical images is a very challenging task, which however is very useful for many clinical applications. For example, magnetic resonance (MR) and transrectal ultrasound (TRUS) image registration is a critical component in MR-TRUS fusion guided prostate interventions. However, due to the huge difference between the image appearances and the large variation in image correspondence, MR-TRUS image registration is a very challenging problem. In this paper, an adversarial image registration (AIR) framework is proposed. By training two deep neural networks simultaneously, one being a generator and the other being a discriminator, we can obtain not only a network for image registration, but also a metric network which can help evaluate the quality of image registration. The developed AIR-net is then evaluated using clinical datasets acquired through image-fusion guided prostate biopsy procedures and promising results are demonstrated.



The authors would like to thank NVIDIA Corporation for the donation of the Titan Xp GPU used for this research.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Pingkun Yan
    • 1
    Email author
  • Sheng Xu
    • 2
  • Ardeshir R. Rastinehad
    • 3
  • Brad J. Wood
    • 2
  1. 1.Department of Biomedical EngineeringRensselaer Polytechnic InstituteTroyUSA
  2. 2.National Institutes of Health, Center for Interventional Oncology, Radiology and Imaging SciencesBethesdaUSA
  3. 3.Icahn School of Medicine at Mount SinaiNew York CityUSA

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