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Published on: January 17, 2018
CT-based manual segmentation and evaluation of paranasal sinuses
S Pirner1, K Tingelhoff, I Wagner
1Clinic und Policlinic of Otolaryngology/Ear, Nose and Throat Surgery, University of Bonn, Bonn, Germany. sim.pirner@freenet.de
Summary
Manual segmentation of CT scans creates 3D models for robot-assisted sinus surgery. These detailed models map risk areas, enhancing safety and enabling future automated segmentation for functional endoscopic sinus surgery (FESS).
Area of Science:
- Medical Imaging
- Robotics in Surgery
- Anatomical Modeling
Background:
- Robot-assisted surgery requires precise anatomical models for navigation.
- Functional endoscopic sinus surgery (FESS) benefits from enhanced visualization of surgical risks.
- Current methods lack detailed risk stratification within segmented anatomical models.
Purpose of the Study:
- To develop a method for manual segmentation of CT datasets for robot-assisted FESS.
- To create patient-specific 3D anatomical models highlighting critical risk areas.
- To establish a database for potential automatic segmentation and to inform robot workspace parameters.
Main Methods:
- Manual segmentation of 50 CT datasets in 150-200 coronal slices.
- Identification and marking of 24 anatomical landmarks.
- Color-coding of segmented regions to delineate diverse risk areas.
- Generation of 3D reconstructions and volumetric measurements.
Main Results:
- Manual segmentation required 8-10 hours per dataset.
- Mean sinus volumes were calculated (e.g., maxillary, frontal, sphenoid).
- Generated 3D models accurately represent individual anatomy and risk zones.
Conclusions:
- Manually segmented 3D models provide detailed anatomical and risk information for robot-assisted FESS.
- These models can serve as a foundation for automated segmentation techniques.
- The risk-based segmentation enhances surgical safety by allowing adjustable robot proximity to critical structures.
