Enhanced Endoscopic Navigation

The significance of endoscopic visualization and navigation is the introduction of a paradigm shift in surgical navigation by using a device present in every endoscopic surgery, namely the endoscope, to improve registration and visualization of anatomy. This will have numerous positive impacts. Most importantly, it will provide an inexpensive, non invasive, radiation-free method to enhance registration accuracy at any point of the procedure. Enhancements in registration will reduce ambiguity for the surgeon during surgery, enhance confidence, and improve workflow by reducing the need to re-register or re-image the patient. The endoscope will also be used as a measurement device to update anatomic models during a procedure. This will not only improve the ability of the surgeon to visualize the progress of the surgery, but it will accrue additional benefits to the patient and hospital, as it may reduce the level of radiation exposure and cost by eliminating the need for intraoperative CT imaging. These aims translate to four main research objectives:

  • Develop video-CT registration algorithms that are accurate to CT resolution
  • Develop methods for surface shape estimation from endoscopic images.
  • Perform comparative evaluation of video-CT-based navigation on patient data.
  • Assess the accuracy and reliability of intra-operative surface estimation on patient data.

  • Publications

    [SNH17]   A Sinha, A Reiter, S Leonard, M Ishii, RH Taylor, GD Hager. “Simultaneous segmentation and correspondence improvement using statistical modes“, Proc. SPIE, 10133, Medical Imaging 2017: Image Processing, 101331B, Orlando, FL (February 24, 2017)

    [BIL16]   SD Billings, A Sinha, A Reiter, S Leonard, M Ishii, GD Hager, RH Taylor. “Anatomically Constrained Video-CT Registration via the V-IMLOP Algorithm“, Lecture Notes in Computer Science, Vol. 9902, pp 133-141, Medical Image Computing and Computer-Assisted Intervention — MICCAI 2016, Athens, Greece (October 02, 2016)

    [REI16]   A Reiter, S Leonard, A Sinha, M Ishii, RH Taylor, GD Hager. “Endoscopic-CT: learning-based photometric reconstruction for endoscopic sinus surgery“, Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978418, San Diego, CA (March 21, 2016).

    [LEO16]   S Leonard, A Reiter, A Sinha, M Ishii, RH Taylor, GD Hager. “Image-based navigation for functional endoscopic sinus surgery using structure from motion“, Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97840V, San Diego, CA (March 21, 2016)


    [SNH16]   A Sinha, S Leonard, A Reiter, M Ishii, RH Taylor, GD Hager. “Automatic segmentation and statistical shape modeling of the paranasal sinuses to estimate natural variations“, Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97840D, San Diego, CA (March 21, 2016)

    Funding Sources

    National Institutes of Health Grant No. R01 EB015530: Enhanced Navigation for Endoscopic Sinus Surgery through Video Analysis, 7/1/2012-6/30/2017