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Adversarial Image Registration with Application for MR and TRUS Image Fusion

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

Abstract

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.

Notes

Acknowledgment

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
  • 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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