Imaging of the Paranasal Sinuses

Labruzzo, Salvatore V.; Aygün, Nafi; Zinreich, S. James

doi:10.1016/j.otc.2015.05.008

Cited by 10 publications

(2 citation statements)

References 36 publications

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“…A meta-analysis by Labruzzo et al 2 demonstrates that experience and enhanced imaging technologies have contributed to significantly decrease the rate of FESS complications. Yet, recent studies report major complication of FESS ranging between 0.31–0.47% of cases and minor complications ranging between 1.37–5.6%.…”

Section: Introductionmentioning

confidence: 99%

“…5 Although progress in imaging and navigation systems have contributed to decrease complications from 8% to 0.31%, 2 Krings et al 6 report that image-guided FESS (IG-FESS) are more likely to have complications and mention that possible reasons for the increased rate of complications include overconfidence in the technology and using these technologies to treat complex cases.…”

Section: Introductionmentioning

confidence: 99%

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Image-based navigation for functional endoscopic sinus surgery using structure from motion

et al. 2016

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Image-based navigation for functional endoscopic sinus surgery using structure from motion

et al. 2016

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A Label‐Efficient Framework for Automated Sinonasal CT Segmentation in Image‐Guided Surgery

Sahu,

Xiao,

Porras

et al. 2024

Otolaryngol.--head neck surg.

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ObjectiveSegmentation, the partitioning of patient imaging into multiple, labeled segments, has several potential clinical benefits but when performed manually is tedious and resource intensive. Automated deep learning (DL)‐based segmentation methods can streamline the process. The objective of this study was to evaluate a label‐efficient DL pipeline that requires only a small number of annotated scans for semantic segmentation of sinonasal structures in CT scans.Study DesignRetrospective cohort study.SettingAcademic institution.MethodsForty CT scans were used in this study including 16 scans in which the nasal septum (NS), inferior turbinate (IT), maxillary sinus (MS), and optic nerve (ON) were manually annotated using an open‐source software. A label‐efficient DL framework was used to train jointly on a few manually labeled scans and the remaining unlabeled scans. Quantitative analysis was then performed to obtain the number of annotated scans needed to achieve submillimeter average surface distances (ASDs).ResultsOur findings reveal that merely four labeled scans are necessary to achieve median submillimeter ASDs for large sinonasal structures—NS (0.96 mm), IT (0.74 mm), and MS (0.43 mm), whereas eight scans are required for smaller structures—ON (0.80 mm).ConclusionWe have evaluated a label‐efficient pipeline for segmentation of sinonasal structures. Empirical results demonstrate that automated DL methods can achieve submillimeter accuracy using a small number of labeled CT scans. Our pipeline has the potential to improve pre‐operative planning workflows, robotic‐ and image‐guidance navigation systems, computer‐assisted diagnosis, and the construction of statistical shape models to quantify population variations.Level of EvidenceN/A

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Vascular Emergency of the Head and Neck

Tang

2020

Atlas of Emergency Neurovascular Imaging

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Imaging of the Paranasal Sinuses

Cited by 10 publications

References 36 publications

Image-based navigation for functional endoscopic sinus surgery using structure from motion

Image-based navigation for functional endoscopic sinus surgery using structure from motion

A Label‐Efficient Framework for Automated Sinonasal CT Segmentation in Image‐Guided Surgery

Vascular Emergency of the Head and Neck

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