An integrated approach to segmentation and nonrigid registration for application in image-guided pelvic radiotherapy Academic Article uri icon


MeSH Major

  • Imaging, Three-Dimensional
  • Prostatic Neoplasms
  • Radiographic Image Interpretation, Computer-Assisted
  • Radiotherapy, Computer-Assisted
  • Tomography, X-Ray Computed
  • Uterine Cervical Neoplasms


  • External beam radiotherapy (EBRT) has become the preferred options for nonsurgical treatment of prostate cancer and cervix cancer. In order to deliver higher doses to cancerous regions within these pelvic structures (i.e. prostate or cervix) while maintaining or lowering the doses to surrounding non-cancerous regions, it is critical to account for setup variation, organ motion, anatomical changes due to treatment and intra-fraction motion. In previous work, manual segmentation of the soft tissues is performed and then images are registered based on the manual segmentation. In this paper, we present an integrated automatic approach to multiple organ segmentation and nonrigid constrained registration, which can achieve these two aims simultaneously. The segmentation and registration steps are both formulated using a Bayesian framework, and they constrain each other using an iterative conditional model strategy. We also propose a new strategy to assess cumulative actual dose for this novel integrated algorithm, in order to both determine whether the intended treatment is being delivered and, potentially, whether or not a plan should be adjusted for future treatment fractions. Quantitative results show that the automatic segmentation produced results that have an accuracy comparable to manual segmentation, while the registration part significantly outperforms both rigid and nonrigid registration. Clinical application and evaluation of dose delivery show the superiority of proposed method to the procedure currently used in clinical practice, i.e. manual segmentation followed by rigid registration.

publication date

  • October 2011



  • Academic Article



  • eng

PubMed Central ID

  • PMC3164526

Digital Object Identifier (DOI)

  • 10.1016/

PubMed ID

  • 21646038

Additional Document Info

start page

  • 772

end page

  • 85


  • 15


  • 5