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.
CITATION STYLE
Sinha, A., Liu, X., Reiter, A., Ishii, M., Hager, G. D., & Taylor, R. H. (2018). Endoscopic Navigation in the Absence of CT Imaging. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11073 LNCS, pp. 64–71). Springer Verlag. https://doi.org/10.1007/978-3-030-00937-3_8
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