A machine learning approach for real-time modelling of tissue deformation in image-guided neurosurgery.

Journal: Artificial intelligence in medicine
Published Date: Jul 24, 2017

Abstract

OBJECTIVES: Accurate reconstruction and visualisation of soft tissue deformation in real time is crucial in image-guided surgery, particularly in augmented reality (AR) applications. Current deformation models are characterised by a trade-off between accuracy and computational speed. We propose an approach to derive a patient-specific deformation model for brain pathologies by combining the results of pre-computed finite element method (FEM) simulations with machine learning algorithms. The models can be computed instantaneously and offer an accuracy comparable to FEM models.

Authors

  • Michele Tonutti
    The Hamlyn Centre for Robotic Surgery, Imperial College London, SW7 2AZ London, United Kingdom. Electronic address: michetonu@gmail.com.
  • Gauthier Gras
    The Hamlyn Centre for Robotic Surgery, Imperial College London, SW7 2AZ London, United Kingdom.
  • Guang-Zhong Yang
    Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China. dgunning@fb.com gzyang@sjtu.edu.cn.

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