Int J Comput Assist Radiol Surg - Automatic segmentation of the nasal cavity and paranasal sinuses from cone-beam CT images.

Tópicos

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Resumo

RPOSE: A patient-specific upper airway model is important for clinical, education, and research applications. Cone-beam computed tomography (CBCT) is used for imaging the upper airway but automatic segmentation is limited by noise and the complex anatomy. A multi-step level set segmentation scheme was developed for CBCT volumetric head scans to create a 3D model of the nasal cavity and paranasal sinuses.METHODS: Gaussian mixture model thresholding and morphological operators are first employed to automatically locate the region of interest and to initialize the active contour. Second, the active contour driven by the Kullback-Leibler (K-L) divergence energy in a level set framework to segment the upper airway. The K-L divergence asymmetry is used to directly minimize the K-L divergence energy on the probability density function of the image intensity. Finally, to refine the segmentation result, an anisotropic localized active contour is employed which defines the local area based on shape prior information. The method was tested on ten CBCT data sets. The results were evaluated by the Dice coefficient, the volumetric overlap error (VOE), precision, recall, and F-score and compared with expert manual segmentation and existing methods.RESULTS: The nasal cavity and paranasal sinuses were segmented in CBCT images with a median accuracy of 95.72 % [93.82-96.72 interquartile range] by Dice, 8.73 % [6.79-12.20] by VOE, 94.69 % [93.80-94.97] by precision, 97.73 % [92.70-98.79] by recall, and 95.72 % [93.82-96.69] by F-score.CONCLUSION: Automated CBCT segmentation of the airway and paranasal sinuses was highly accurate in a test sample of clinical scans. The method may be useful in a variety of clinical, education, and research applications.

Resumo Limpo

rpose patientspecif upper airway model import clinic educ research applic conebeam comput tomographi cbct use imag upper airway automat segment limit nois complex anatomi multistep level set segment scheme develop cbct volumetr head scan creat d model nasal caviti paranas sinusesmethod gaussian mixtur model threshold morpholog oper first employ automat locat region interest initi activ contour second activ contour driven kullbackleibl kl diverg energi level set framework segment upper airway kl diverg asymmetri use direct minim kl diverg energi probabl densiti function imag intens final refin segment result anisotrop local activ contour employ defin local area base shape prior inform method test ten cbct data set result evalu dice coeffici volumetr overlap error voe precis recal fscore compar expert manual segment exist methodsresult nasal caviti paranas sinus segment cbct imag median accuraci interquartil rang dice voe precis recal fscoreconclus autom cbct segment airway paranas sinus high accur test sampl clinic scan method may use varieti clinic educ research applic

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