Automatic needle tracking using Mask R-CNN for MRI-guided percutaneous interventions.

Journal: International journal of computer assisted radiology and surgery
Published Date: Jul 16, 2020

Abstract

PURPOSE: Accurate needle tracking provides essential information for MRI-guided percutaneous interventions. Passive needle tracking using MR images is challenged by variations of the needle-induced signal void feature in different situations. This work aimed to develop an automatic needle tracking algorithm for MRI-guided interventions based on the Mask Region Proposal-Based Convolutional Neural Network (R-CNN).

Authors

  • Xinzhou Li
    Department of Radiological Sciences, University of California Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA.
  • Adam S Young
    Department of Radiological Sciences, University of California Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA.
  • Steven S Raman
    Department of Radiologic Sciences David Geffen School of Medicine, University of California Los Angeles CA.
  • David S Lu
    Department of Radiological Sciences, University of California Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA.
  • Yu-Hsiu Lee
    Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Los Angeles, CA, USA.
  • Tsu-Chin Tsao
    Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Los Angeles, CA, USA.
  • Holden H Wu
    Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.

Keywords

External Resources

Popular Topics
Recent Journals