Quality Assessment of Non-Rigid Registration Methods for Atlas-Based Segmentation in Head-Neck Radiotherapy

Abstract

In this paper we compare three non-rigid registration methods for atlas-based segmentation: B-splines, morphons and a combination of morphons and demons. To assess the quality of each method, we use a data set of four patients, containing for each patient the computed tomography (CT) image and a manual segmentation of the organs at risk performed by an expert of the head and neck anatomy. Non-rigid registration algorithms have been used to match the patient and atlas images. Each deformation field, resulting from the non-rigid deformation, have been applied on the masks corresponding to segmented regions in the atlas. The atlas based segmented masks have been compared to manual segmentations performed by the expert. The results show that the combined method (morphons + demons) achieves the best performances on this dataset resulting in an average improvement of 6% with respect to morphons and 18% with respect to B-spline.