A random walk approach for multiatlas-based segmentation

Although atlas-based methods simplify the segmentation process by making it more automated, such methods are often very sensitive to the computationally expensive image registration step. Also, existing methods based on a parametric deformation model may fail when the transformation between the atlas and target images can not be properly described with this model. This paper presents a novel and efficient atlas-based segmentation method based on random walks. Unlike most atlas-based approaches, this method combines the registration and label propagation steps in a single efficient framework and does not depend on a specific deformation model. Experiments conducted on benchmark images show the accuracy and efficiency of our method.