Related Experiment Video
Updated: Jun 10, 2026
Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform
Published on: March 24, 2022
A geodesic deformable model for automatic segmentation of image sequences applied to radiation therapy
1E.T.S.Ingenieros Industriales, Universidad de Castilla-La Mancha, Avda. Camilo José Cela, 3, 13071, Ciudad Real, Spain. gloria.bueno@uclm.es
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.
