title: Automatic delineation of on-line head-and-neck computed tomography images: toward on-line adaptive radiotherapy.
abstract: OBJECTIVE
  To develop and validate a fully automatic region-of-interest (ROI) delineation method for on-line adaptive radiotherapy.
  
  
  METHODS
  On-line adaptive radiotherapy requires a robust and automatic image segmentation method to delineate ROIs in on-line volumetric images. We have implemented an atlas-based image segmentation method to automatically delineate ROIs of head-and-neck helical computed tomography images. A total of 32 daily computed tomography images from 7 head-and-neck patients were delineated using this automatic image segmentation method. Manually drawn contours on the daily images were used as references in the evaluation of automatically delineated ROIs. Two methods were used in quantitative validation: (1) the dice similarity coefficient index, which indicates the overlapping ratio between the manually and automatically delineated ROIs; and (2) the distance transformation, which yields the distances between the manually and automatically delineated ROI surfaces.
  
  
  RESULTS
  Automatic segmentation showed agreement with manual contouring. For most ROIs, the dice similarity coefficient indexes were approximately 0.8. Similarly, the distance transformation evaluation results showed that the distances between the manually and automatically delineated ROI surfaces were mostly within 3 mm. The distances between two surfaces had a mean of 1 mm and standard deviation of <2 mm in most ROIs.
  
  
  CONCLUSIONS
  With atlas-based image segmentation, it is feasible to automatically delineate ROIs on the head-and-neck helical computed tomography images in on-line adaptive treatments.
authors.affiliation: Department of Radiation Oncology, William Beaumont Hospital, Royal Oak, MI 48073, USA. tiezhi.zhang@beaumont.edu
authors.forename: Tiezhi
authors.initials: T
authors.lastname: Zhang
authors.forename: Yuwei
authors.initials: Y
authors.lastname: Chi
authors.forename: Elisa
authors.initials: E
authors.lastname: Meldolesi
authors.forename: Di
authors.initials: D
authors.lastname: Yan
mesh_terms: D005240:Feasibility Studies; D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging* / Q000532:radiotherapy*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008488:Medical Illustration; D011878:Radiotherapy / Q000379:methods; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D036542:Tomography, Spiral Computed / Q000379:methods*