Anatomically Constrained Video-CT Registration via the V-IMLOP Algorithm
October 25, 2016 Β· Declared Dead Β· π International Conference on Medical Image Computing and Computer-Assisted Intervention
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Authors
Seth D. Billings, Ayushi Sinha, Austin Reiter, Simon Leonard, Masaru Ishii, Gregory D. Hager, Russell H. Taylor
arXiv ID
1610.07931
Category
cs.CV: Computer Vision
Citations
12
Venue
International Conference on Medical Image Computing and Computer-Assisted Intervention
Last Checked
4 months ago
Abstract
Functional endoscopic sinus surgery (FESS) is a surgical procedure used to treat acute cases of sinusitis and other sinus diseases. FESS is fast becoming the preferred choice of treatment due to its minimally invasive nature. However, due to the limited field of view of the endoscope, surgeons rely on navigation systems to guide them within the nasal cavity. State of the art navigation systems report registration accuracy of over 1mm, which is large compared to the size of the nasal airways. We present an anatomically constrained video-CT registration algorithm that incorporates multiple video features. Our algorithm is robust in the presence of outliers. We also test our algorithm on simulated and in-vivo data, and test its accuracy against degrading initializations.
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