A geodesic deformable model for automatic segmentation of image sequences applied to radiation therapy

Int J Comput Assist Radiol Surg. 2011 May;6(3):341-50. doi: 10.1007/s11548-010-0513-9. Epub 2010 Jul 20.


Purpose: Organ motion should be taken into account for image-guided fractionated radiotherapy. A deformable segmentation and registration method was developed for inter-and intra-fraction organ motion planning and evaluation.

Methods: Energy minimizing active models were synthesized for tracking a set of organs delineated by regions of interest (ROI) in radiotherapy treatment. The initial model consists of a surface deformed to match the ROI contour by geometrical properties, following a heat flow model. The deformable segmentation model was tested using a Shepp-Logan head CT simulation, and different quantitative metrics were applied such as ROC analysis, Jaccard index, Dice coefficient and Hausdorff distance.

Results: Experimental evaluation of automated versus manual segmentation was done for the cardiac, thoracic and pelvic regions. The method has been quantitatively validated, obtaining an average of 93.3 and 99.2% for the sensitivity and specificity, respectively, 90.79% for the Jaccard index, 95.15% for the Dice coefficient and 0.96% mm for the Hausdorff distance.

Conclusions: Model-based deformable segmentation was developed and tested for image-guided radiotherapy treatment planning. The method is efficient, robust and has sufficient accuracy for 2D CT data without markers.

Publication types

  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Algorithms
  • Heart / radiation effects
  • Humans
  • Lung / radiation effects
  • Models, Anatomic*
  • Motion
  • ROC Curve
  • Radiography, Abdominal
  • Radiography, Thoracic
  • Radiotherapy, Computer-Assisted / methods*
  • Sensitivity and Specificity
  • Tomography, X-Ray Computed