CT-based manual segmentation and evaluation of paranasal sinuses.

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.

[Evaluation of force data with a force/torque sensor during FESS. A step towards robot-assisted surgery]. / Sensorbasierte Messung mechanischer Kräfte am Endoskop während FESS. Schritte zur robotergestützten Nasennebenhöhlenchirurgie.

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.

3D endoscopic approach for endonasal sinus surgery.

Functional endoscopic sinus surgery (FESS) is a minimal invasive approach adopted in case of chronic sinusitis (inflammation of the paranasal sinuses). The paranasal sinuses are hollow structures within the bones surrounding the nasal cavity. During FESS the surgeon moves the endoscope and other surgical instruments within the nasal cavity following specific paths to approach each one of the paranasal sinuses. The purpose of this study was to reconstruct these paths to access the paranasal sinuses using volumetric CT data. The results will be used for Finite Element modeling and simulations for Robot Assisted Endonasal Surgery.

Comparison between manual and semi-automatic segmentation of nasal cavity and paranasal sinuses from CT images.

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.