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.