Refined Segmentation in Statistical Multiscale Framework

Based on an effective statistical segmentation methodology using a deformable medial model, a local scale deformation approach is developed to refine the global scale segmentation results within a multiscale framework. In the local scale segmentation, the probabilistic variations of locally aligned shape residuals from the global scale are learned from proper training followed by a posterior probability optimization in local regions. The resulting finer scale deformation improves the accuracy of the segmentation results, shown by experimental study on 3D CT images of the male pelvic area in day-to-day adaptive radiotherapy.