Endoscopic Navigation in the Absence of CT Imaging
Clinical examinations that involve endoscopic exploration of the nasal cavity and sinuses often do not have a reference image to provide structural context to the clinician. In this paper, we present a system for navigation during clinical endoscopic exploration in the absence of computed tomography (CT) scans by making use of shape statistics from past CT scans. Using a deformable registration algorithm along with dense reconstructions from video, we show that we are able to achieve submillimeter registrations in in-vivo clinical data and are able to assign confidence to these registrations using confidence criteria established using simulated data.
This work was funded by NIH R01-EB015530, NSF Graduate Research Fellowship Program, an Intuitive Surgical, Inc. fellowship, and JHU internal funds.
- 3.Beichel, R.R., et al.: Data from QIN-HEADNECK. The Cancer Imaging Archive (2015)Google Scholar
- 4.Bosch, W.R., Straube, W.L., Matthews, J.W., Purdy, J.A.: Data from head-neck\_cetuximab. The Cancer Imaging Archive (2015)Google Scholar
- 8.Sinha, A., Reiter, A., Leonard, S., Ishii, M., Hager, G.D., Taylor, R.H.: Simultaneous segmentation and correspondence improvement using statistical modes. In: Proceedings of SPIE, vol. 10133, pp. 101 331B–101 331B–8 (2017)Google Scholar
- 10.Sinha, A., et al.: The deformable most-likely-point paradigm. Med. Image Anal. (Submitted)Google Scholar
- 12.Mardia, K.V., Jupp, P.E.: Directional statistics. Wiley Series in Probability and Statistics, pp. 1–432. Wiley, Hoboken (2008)Google Scholar
- 13.Reiter, A., Leonard, S., Sinha, A., Ishii, M., Taylor, R.H., Hager, G.D.: Endoscopic-CT: learning-based photometric reconstruction for endoscopic sinus surgery. In: Proceedings of SPIE, vol. 9784, pp. 978 418–978 418–6 (2016)Google Scholar