Computerized Identification of Airway Wall in CT Examinations Using a 3D Active Surface Evolution Approach

摘要: Abstract Airway diseases (e.g., asthma, emphysema, and chronic bronchitis) are extremely common worldwide. Any morphological variations (abnormalities) of airways may physically change airflow ultimately affect the ability lungs in gas exchange. In this study, we describe a novel algorithm aimed to automatically identify airway walls depicted on CT images. The underlying idea is place three-dimensional (3D) surface model within regions thereafter allow evolve (deform) under predefined external internal forces location where these reach state balance. By taking advantage geometric density characteristics walls, evolution procedure performed distance gradient field stops at with highest contrast. performance scheme was quantitatively evaluated from several perspectives. First, assessed accuracy developed using dedicated lung phantom wall estimation compared it traditional full-width half maximum (FWHM) method. study shows that has an error ranging 0.04 mm 0.36 mm, which much smaller than FWHM method 0.16 mm 0.84 mm. Second, results obtained by those manually delineated experienced (>30 years) radiologist clinical chest examinations, showing mean difference 0.084 mm. particular, sensitivity different reconstruction kernels real examinations. For ‘lung’, ‘bone’ ‘standard’ kernels, average thicknesses computed were 1.302 mm, 1.333 mm 1.339 mm, respectively. Our preliminary experiments showed had reasonable estimation. examination, took around 4 min for inner outer modern PC.