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Adversarial Image Registration with Application for MR and TRUS Image Fusion

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Machine Learning in Medical Imaging (MLMI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11046))

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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.

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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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Correspondence to Pingkun Yan .

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Yan, P., Xu, S., Rastinehad, A.R., Wood, B.J. (2018). Adversarial Image Registration with Application for MR and TRUS Image Fusion. In: Shi, Y., Suk, HI., Liu, M. (eds) Machine Learning in Medical Imaging. MLMI 2018. Lecture Notes in Computer Science(), vol 11046. Springer, Cham. https://doi.org/10.1007/978-3-030-00919-9_23

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  • DOI: https://doi.org/10.1007/978-3-030-00919-9_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00918-2

  • Online ISBN: 978-3-030-00919-9

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