Automatic Delineation of On-Line Head-And-Neck Computed Tomography Images: Toward On-Line Adaptive Radiotherapy

Tiezhi Zhang, Yuwei Chi, Elisa Meldolesi, Di Yan

Research output: Contribution to journalArticlepeer-review

148 Scopus citations

Abstract

Purpose: To develop and validate a fully automatic region-of-interest (ROI) delineation method for on-line adaptive radiotherapy. Methods and Materials: 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. Conclusion: 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.

Original languageEnglish
Pages (from-to)522-530
Number of pages9
JournalInternational Journal of Radiation Oncology Biology Physics
Volume68
Issue number2
DOIs
StatePublished - Jun 1 2007

Keywords

  • Adaptive radiotherapy
  • Deformable image registration
  • Image-guided radiotherapy
  • Region-of-interest delineation

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