Image-based navigation for functional endoscopic sinus surgery using structure from motion
Open accessAbstract
Functional Endoscopic Sinus Surgery (FESS) is a challenging procedure for otolaryngologists and is the main surgical approach for treating chronic sinusitis, to remove nasal polyps and open up passageways. To reach the source of the problem and to ultimately remove it, the surgeons must often remove several layers of cartilage and tissues. Often, the cartilage occludes or is within a few millimeters of critical anatomical structures such as nerves, arteries and ducts. To make FESS safer, surgeons use navigation systems that register a patient to his/her CT scan and track the position of the tools inside the patient. Current navigation systems, however, suffer from tracking errors greater than 1 mm, which is large when compared to the scale of the sinus cavities, and errors of this magnitude prevent from accurately overlaying virtual structures on the endoscope images. In this paper, we present a method to facilitate this task by 1) registering endoscopic images to CT data and 2) overlaying areas of interests on endoscope images to improve the safety of the procedure. First, our system uses structure from motion (SfM) to generate a small cloud of 3D points from a short video sequence. Then, it uses iterative closest point (ICP) algorithm to register the points to a 3D mesh that represents a section of a patients sinuses. The scale of the point cloud is approximated by measuring the magnitude of the endoscope's motion during the sequence. We have recorded several video sequences from five patients and, given a reasonable initial registration estimate, our results demonstrate an average registration error of 1.21 mm when the endoscope is viewing erectile tissues and an average registration error of 0.91 mm when the endoscope is viewing non-erectile tissues. Our implementation SfM + ICP can execute in less than 7 seconds and can use as few as 15 frames (0.5 second of video). Future work will involve clinical validation of our results and strengthening the robustness to initial guesses and erectile tissues.
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Image-based navigation techniques do not require specialized equipment but rely on traditional intra-operative imaging.
“Image-based navigation techniques do not require specialized equipment but rely on traditional intra-operative imaging that enabled the minimally invasive technique in the first place.”
From The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective (2021) · cites this paper as Leonard et al., 2016
The Introduction and Method describe navigation based on endoscopic images registered to a CT scan, supporting the imaging portion of the claim. But the Method says the endoscope’s motion is tracked with a 6DOF magnetic tracker to estimate reconstruction scale, and the Results describe attaching that tracker to the endoscope. This contradicts the claim that the technique requires no specialized equipment. Page numbers were not available in our copy. (LLM, conf=0.88)
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Research works printed but not linked: 19
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Cited by
Sorted by citations
Unberath, M., Gao, C., Hu, Y., Judish, M., Taylor, R. H., Armand, M., & Grupp, R. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.716007 · Cited by 102 · see citing works
Self-citation
- Authors
- Leonard, S., Reiter, A., Sinha, A., Ishii, M., Taylor, R. H., & Hager, G. D.
- Affiliations
- Johns Hopkins Univ. (United States)
- Johns Hopkins University (US)
- Venue
- SPIE Proceedings
- Publisher
- SPIE
- Published
- 2016-03-21
- Volume
- 9784
- Pages
- 97840V
- ISSN
- 0277-786X (ISSN-L)
- PMC
- PMC5720359
- OpenAlex
- W2306029903
- Cited by
- 35
- References
- 3
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 4 · 2023: 3 · 2022: 1 · 2021: 4 · 2020: 8 · 2019: 4 · 2018: 4 · 2016: 2
- FWCI
- 1.16
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Biomedical Imaging and Bioengineering (R01EB015530)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Augmented Reality Applications
- Health Sciences › Medicine › Otorhinolaryngology › Sinusitis and nasal conditions
- Keywords
- functional endoscopic sinus surgery, image-guided surgery, structure from motion, CT registration, iterative closest point, point cloud registration, erectile tissue, surgical navigation, chronic rhinosinusitis, nasal polyps, registration error
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