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Taylor, D., Doorly, D. and Schroter, R. (2010) Inflow Boundary Profile Prescription for Numerical Simulation of Nasal Airflow. Journal of the Royal Society Interface, 7, 515-527.

has been cited by the following article:

  • TITLE: Strategies for Segmenting the Upper Airway in Cone-Beam Computed Tomography (CBCT) Data

    AUTHORS: N. Kabaliuk, A. Nejati, C. Loch, D. Schwass, J. E. Cater, M. C. Jermy

    KEYWORDS: Cone Beam CT, CBCT, Segmentation, Upper Airway, Nasal Cavity, Pharyngeal Airway

    JOURNAL NAME: Open Journal of Medical Imaging, Vol.7 No.4, November 14, 2017

    ABSTRACT: The wide availability, low radiation dose and short acquisition time of Cone-Beam CT (CBCT) scans make them an attractive source of data for compiling databases of anatomical structures. However CBCT has higher noise and lower contrast than helical slice CT, which makes segmentation more challenging and the optimal methods are not yet known. This paper evaluates several methods of segmenting airway geometries (nares, nasal cavities and pharynx) from typical dental quality head and neck CBCT data. The nasal cavity has narrow and intricate passages and is separated from the paranasal sinuses by thin walls, making it is susceptible to either over- or under-segmentation. The upper airway was split into two: the nasal cavity and the pharyngeal region (nasopharynx to larynx). Each part was segmented using global thresholding, multi-step level-set, and region competition methods (the latter using thresholding, clustering and classification initialisation and edge attraction techniques). The segmented 3D surfaces were evaluated against a reference manual segmentation using distance-, overlap- and volume-based metrics. Global thresholding, multi-step level-set, and region competition all gave satisfactory results for the lower part of the airway (nasopharynx to larynx). Edge attraction failed completely. A semi-automatic region-growing segmentation with multi-thresholding (or classification) initialization offered the best quality segmentation. With some minimal manual editing, it resulted in an accurate upper airway model, as judged by the similarity and volumetric indices, while being the least time consuming of the semi-automatic methods, and relying the least on the operator’s expertise.