title: Comparison between manual and semi-automatic segmentation of nasal cavity and paranasal sinuses from CT images.
abstract: 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.
authors.affiliation: Clinic and Policlinic of Otolaryngology/Ear, Nose and Throat Surgery, University of Bonn, Germany. kathrin.tingelhoff@ukb.uni-bonn.de
authors.forename: K
authors.initials: K
authors.lastname: Tingelhoff
authors.forename: A I
authors.initials: AI
authors.lastname: Moral
authors.forename: M E
authors.initials: ME
authors.lastname: Kunkel
authors.forename: M
authors.initials: M
authors.lastname: Rilk
authors.forename: I
authors.initials: I
authors.lastname: Wagner
authors.forename: K G
authors.initials: KG
authors.lastname: Eichhorn
authors.forename: F M
authors.initials: FM
authors.lastname: Wahl
authors.forename: F
authors.initials: F
authors.lastname: Bootz
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D009296:Nasal Cavity / Q000000981:diagnostic imaging*; D015588:Observer Variation; D010256:Paranasal Sinuses / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*