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Background: Having one hand occupied with the endoscope is the major disadvantage for the surgeon when it comes to functional endoscopic sinus surgery (FESS). Only the other hand is free to use the surgical instruments. Tiredness or frequent instrument changes can thus lead to shaky endoscopic images., Methods: We collected the pose data (position and orientation) of the rigid 0° endoscope and all the instruments used in 16 FESS procedures with manual endoscope guidance as well as robot-assisted endoscope guidance. In combination with the DICOM CT data, we tracked the endoscope poses and workspaces using self-developed tracking markers., Results: All surgeries were performed once with the robot and once with the surgeon holding the endoscope. Looking at the durations required, we observed a decrease in the operating time because one surgeon doing all the procedures and so a learning curve occurred what we expected. The visual inspection of the specimens showed no damages to any of the structures outside the paranasal sinuses., Conclusion: Robot-assisted endoscope guidance in sinus surgery is possible. Further CT data, however, are desirable for the surgical analysis of a tracker-based navigation within the anatomic borders. Our marker-based tracking of the endoscope as well as the instruments makes an automated endoscope guidance feasible. On the subjective side, we see that RASS brings a relief for the surgeon.

Background: For the further development of robot-assisted endoscope guidance in functional endoscopic sinus surgery (FESS), ground data about the workspaces and endoscope movements in conventional FESS are needed., Methods: Applying a self-developed marker-based tracking system, we collected the pose data (position and orientation) of the endoscope and all other instruments used in five real sinus surgeries., Results: The automated segmentation of the endoscope poses shows the shape of a hourglass, with a pivot region or pivot point at the 'waistline' of the hourglass, close to the nasal entrance in the nasal dome., Conclusion: We were able to identify a pivot point at the waistline of the segmented endoscope poses. The size of the pivot point corresponds with the diameter of the 4 mm endoscope. Because of the reduction to four degrees of freedom for endoscope motions (three rotations and one translation), easier and safer robot-assisted endoscope guidance becomes feasible., (Copyright © 2014 John Wiley & Sons, Ltd.)

Manual segmentation of computed tomography (CT) datasets was performed for robot-assisted endoscope movement during functional endoscopic sinus surgery (FESS). Segmented 3D models are needed for the robots' workspace definition. A total of 50 preselected CT datasets were each segmented in 150-200 coronal slices with 24 landmarks being set. Three different colors for segmentation represent diverse risk areas. Extension and volumetric measurements were performed. Three-dimensional reconstruction was generated after segmentation. Manual segmentation took 8-10 h for each CT dataset. The mean volumes were: right maxillary sinus 17.4 cm(3), left side 17.9 cm(3), right frontal sinus 4.2 cm(3), left side 4.0 cm(3), total frontal sinuses 7.9 cm(3), sphenoid sinus right side 5.3 cm(3), left side 5.5 cm(3), total sphenoid sinus volume 11.2 cm(3). Our manually segmented 3D-models present the patient's individual anatomy with a special focus on structures in danger according to the diverse colored risk areas. For safe robot assistance, the high-accuracy models represent an average of the population for anatomical variations, extension and volumetric measurements. They can be used as a database for automatic model-based segmentation. None of the segmentation methods so far described provide risk segmentation. The robot's maximum distance to the segmented border can be adjusted according to the differently colored areas.

Background: To relieve the surgeon during functional endoscopic endonasal sinus surgery (FESS), the endoscope should be guided by autonomous robot assistance. The surgeon will thus have two hands free for suctioning and manipulation during FESS., Patients/methods: With a force/torque sensor mounted on the endoscope, we measured forces in six degrees of freedom in five cadaver heads and in 20 actual endoscopic sinus procedures. On the cadaver heads we performed complete endoscopic endonasal dissection of all paranasal sinuses. All forces at the endoscope were monitored continuously., Results: The mean forces occurring at the endoscope were 3.2 N. There were only slight differences between the in vivo and ex vivo data. We measured peak forces up to 25.2 N. In 95% of all cases, forces were lower than 7 N., Conclusion: Forces up to 7 N are sufficient for endoscopic guidance during FESS. Peak forces are distinctive for endoscopic guidance by humans and could be optimised by sensor-based intraoperative robot guidance. Higher forces are required for surgical endoscopy of the frontal and maxillary sinuses compared with the ethmoid sinuses.

Segmentation of medical image data is getting more and more important over the last years. The results are used for diagnosis, surgical planning or workspace definition of robot-assisted systems. The purpose of this paper is to find out whether manual or semi-automatic segmentation is adequate for ENT surgical workflow or whether fully automatic segmentation of paranasal sinuses and nasal cavity is needed. We present a comparison of manual and semi-automatic segmentation of paranasal sinuses and the nasal cavity. Manual segmentation is performed by custom software whereas semi-automatic segmentation is realized by a commercial product (Amira). For this study we used a CT dataset of the paranasal sinuses which consists of 98 transversal slices, each 1.0 mm thick, with a resolution of 512 x 512 pixels. For the analysis of both segmentation procedures we used volume, extension (width, length and height), segmentation time and 3D-reconstruction. The segmentation time was reduced from 960 minutes with manual to 215 minutes with semi-automatic segmentation. We found highest variances segmenting nasal cavity. For the paranasal sinuses manual and semi-automatic volume differences are not significant. Dependent on the segmentation accuracy both approaches deliver useful results and could be used for e.g. robot-assisted systems. Nevertheless both procedures are not useful for everyday surgical workflow, because they take too much time. Fully automatic and reproducible segmentation algorithms are needed for segmentation of paranasal sinuses and nasal cavity.