title: Robust segmentation and anatomical labeling of the airway tree from thoracic CT scans.
abstract: A method for automatic extraction and labeling of the airway tree from thoracic CT scans is presented and extensively evaluated on 150 scans of clinical dose, low dose and ultra-low dose data, in inspiration and expiration from both relatively healthy and severely ill patients. The method uses adaptive thresholds while growing the airways and it is shown that this strategy leads to a substantial increase in the number, total length and number of correctly labeled airways extracted. From inspiration scans on average 170 branches are found, from expiration scans 59.
authors.affiliation: Image Sciences Institute, University Medical Center Utrecht, The Netherlands.
authors.forename: Bram
authors.initials: B
authors.lastname: van Ginneken
authors.forename: Wouter
authors.initials: W
authors.lastname: Baggerman
authors.forename: Eva M
authors.initials: EM
authors.lastname: van Rikxoort
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*