Learning deep similarity metric for 3D MR-TRUS image registration.

Journal: International journal of computer assisted radiology and surgery
Published Date: Oct 31, 2018

Abstract

PURPOSE: The fusion of transrectal ultrasound (TRUS) and magnetic resonance (MR) images for guiding targeted prostate biopsy has significantly improved the biopsy yield of aggressive cancers. A key component of MR-TRUS fusion is image registration. However, it is very challenging to obtain a robust automatic MR-TRUS registration due to the large appearance difference between the two imaging modalities. The work presented in this paper aims to tackle this problem by addressing two challenges: (i) the definition of a suitable similarity metric and (ii) the determination of a suitable optimization strategy.

Authors

  • Grant Haskins
    Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.
  • Jochen Kruecker
    Philips Research North America, Cambridge, MA, 02141, USA.
  • Uwe Kruger
  • Sheng Xu
    School of Physics and Information Engineering, Jiangsu Second Normal University, Nanjing, 211200, China.
  • Peter A Pinto
    Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
  • Brad J Wood
    Center for Interventional Oncology, Radiology & Imaging Sciences, National Institutes of Health, Bethesda, MD, 20892, USA.
  • Pingkun Yan
    Philips Research North America, Briarcliff Manor, NY 10510, USA.

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