Geodesic Active Contours with Adaptive Configuration for Cerebral Vessel and Aneurysm Segmentation

Active contour is a popular technique for vascular segmentation. However, existing active contour segmentation methods require users to set values for various parameters, which requires insights to the method´s mathematical formulation. Manual tuning of these parameters to optimize segmentation results is laborious for clinicians who often lack in-depth knowledge of the segmentation algorithms. Moreover, a global parameter setting applied to all voxels of an input image can hardly achieve optimized results due to vessels´ high appearance variability caused by the contrast agent in homogeneity and noises. In this paper, we present a method which adaptively configures parameters for Geodesic Active Contours (GAC). The proposed method leverages shape filtering to produce a parameter image, each voxel of which is used to set parameters of GAC for the corresponding voxel of an input image. An iterative process is further developed to improve the accuracy of the shape-based parameter image. An evaluation study over 8 clinical datasets demonstrates that our method achieves greater segmentation accuracy than two popular active contour methods with manually optimized parameters.