Corpus ID: 3558264

i3PosNet: Instrument Pose Estimation from X-Ray

@article{Kgler2018i3PosNetIP,
  title={i3PosNet: Instrument Pose Estimation from X-Ray},
  author={David K{\"u}gler and Andrei Stefanov and Anirban Mukhopadhyay},
  journal={ArXiv},
  year={2018},
  volume={abs/1802.09575}
}
  • David Kügler, Andrei Stefanov, Anirban Mukhopadhyay
  • Published in ArXiv 2018
  • Computer Science, Engineering
  • Performing delicate Minimally Invasive Surgeries (MIS) forces surgeons to accurately assess the position and orientation (pose) of surgical instruments. In current practice, this pose information is provided by conventional tracking systems (optical and electro-magnetic). Two challenges render these systems inadequate for minimally invasive bone surgery: the need for instrument positioning with high precision and occluding tissue blocking the line of sight. Fluoroscopic tracking is limited by… CONTINUE READING

    Create an AI-powered research feed to stay up to date with new papers like this posted to ArXiv

    6
    Twitter Mentions

    Citations

    Publications citing this paper.
    SHOWING 1-5 OF 5 CITATIONS

    Enabling machine learning in X-ray-based procedures via realistic simulation of image formation

    VIEW 2 EXCERPTS
    CITES BACKGROUND

    High-precision evaluation of electromagnetic tracking

    VIEW 1 EXCERPT

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 37 REFERENCES

    A review of 3D/2D registration methods for image-guided interventions

    VIEW 5 EXCERPTS
    HIGHLY INFLUENTIAL

    Standardized evaluation methodology for 2-D-3-D registration

    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL

    Anonymous submission

    • D. Kügler, M. Jastrzebski, A. Mukhopadhyay
    • 2018.
    • 2018
    VIEW 2 EXCERPTS

    A survey on deep learning in medical image analysis

    VIEW 3 EXCERPTS

    FERA 2017 - Addressing Head Pose in the Third Facial Expression Recognition and Analysis Challenge