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Resolve Intraoperative Brain Shift as Imitation Game

  • Xia Zhong
  • Siming Bayer
  • Nishant Ravikumar
  • Norbert Strobel
  • Annette Birkhold
  • Markus Kowarschik
  • Rebecca Fahrig
  • Andreas Maier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11042)

Abstract

Soft tissue deformation induced by craniotomy and tissue manipulation (brain shift) limits the use of preoperative image overlay in an image-guided neurosurgery, and therefore reduces the accuracy of the surgery as a consequence. An inexpensive modality to compensate for the brain shift in real-time is Ultrasound (US). The core subject of research in this context is the non-rigid registration of preoperative MR and intraoperative US images. In this work, we propose a learning based approach to address this challenge. Resolving intraoperative brain shift is considered as an imitation game, where the optimal action (displacement) for each landmark on MR is trained with a multi-task network. The result shows a mean target error of 1.21 ± 0.55 mm.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Xia Zhong
    • 1
  • Siming Bayer
    • 1
  • Nishant Ravikumar
    • 1
  • Norbert Strobel
    • 4
  • Annette Birkhold
    • 2
  • Markus Kowarschik
    • 2
  • Rebecca Fahrig
    • 2
  • Andreas Maier
    • 1
    • 3
  1. 1.Pattern Recognition LabFriedrich-Alexander University Erlangen-NürnbergErlangenGermany
  2. 2.Siemens Healthcare GmbHForchheimGermany
  3. 3.Erlangen Graduate School in Advanced Optical Technologies (SAOT)ErlangenGermany
  4. 4.Fakultät für ElektrotechnikHochschule für angewandte Wissenschaften Würzburg-SchweinfurtWürzburg and SchweinfurtGermany

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