3D Meshless Prostate Segmentation and Registration in Image Guided Radiotherapy

Image Guided Radiation Therapy (IGRT) improves radiation therapy for prostate cancer by facilitating precise radiation dose coverage of the object of interest, and minimizing dose to adjacent normal organs. In an effort to optimize IGRT, we developed a fast segmentation-registration-segmentation framework to accurately and efficiently delineate the clinically critical objects in Cone Beam CT images obtained during radiation treatment. The proposed framework started with deformable models automatically segmenting the prostate, bladder, and rectum in planning CT images. All models were built around seed points and involved in the CT image under the influence of image features using the level set formulation. The deformable models were then converted into meshless point sets and underwent a 3D non rigid registration from the planning CT to the treatment CBCT. The motion of deformable models during the registration was constrained by the global shape prior on the target surface during the deformation. The meshless formulation provided a convenient interface between deformable models and the image feature based registration method. The final registered deformable models in the CBCT domain were further refined using the interaction between objects and other available image features. The segmentation results for 15 data sets has been included in the validation study, compared with manual segmentations by a radiation oncologist. The automatic segmentation results achieved a satisfactory convergence with manual segmentations and met the speed requirement for on line IGRT.