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.
Highlights
This paper evaluates several methods of segmenting airway geometries from typical dental quality head and neck ConeBeam Computed Tomography (CT) (CBCT) data
Respiratory illness affects a substantial number of people worldwide with the top five respiratory diseases accounting for 17.4% of all deaths and 13.3% of all Disability-Adjusted Life Years (DALYs) [1]
For segmenting the human upper airway from a typical dental CBCT scan of high noise and low contrast, the following methods were tested: Global thresholding was the most straightforward, but the optimum threshold value was highly dependent on image quality and the user’s familiarity with the anatomy
Summary
Respiratory illness affects a substantial number of people worldwide with the top five respiratory diseases accounting for 17.4% of all deaths and 13.3% of all Disability-Adjusted Life Years (DALYs) [1]. Breathing therapy devices are frequently used to treat breathing disorders such as chronic obstructive pulmonary disease (COPD) and obstructive sleep apnoea (OSA), and to assist premature babies as their lungs develop. The nasal cavity in particular, shows significant interpersonal variation [3] [4] [5] [6]. Knowledge of this variation is important for the design of better therapeutic devices, and requires segmentation of the airway from many individual scans
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