Segmentation and annotation of tumors in CT scans of the brain is a cumbersome time-consuming task for medical experts. Carefully annotated data can be used to build training data sets for machine learning frameworks, with the ultimate goal to fully automate this process. This thesis focuses on acceleration of the annotation process by implementation of an interactive accelerated segmentation model rather than implementation or evaluation of the machine learning part. The Chan-Vese model is an active contour model which can be used to detect objects for which the boundaries are not necessarily defined by gradient. An energy functional is minimized by evolvement of the contour. Evolvement of the contour in a numerical approximation, which uses finite differences and a level set formulation, is determined by solving a Partial differential equation (PDE) with an iterative solver. This thesis presents implementations for both 2D and 3D which use Successive over-relaxation (SOR) to solve the PDE. This computationally intensive task benefits from acceleration to keep the feedback loop, in the process of tuning parameters and convergence to the searched segmentation, as short as possible. The effect of varying the different parameters of the model are visualized for different examples images to allow for educated guesses. Accelerated implementations which leverage Compute Unified Device Architecture (CUDA) on a Graphics processing unit (GPU) are presented and compared to sequential and multithreaded OpenMP implementations. Evaluation of the CUDA implementations with single precision on a POWER8 platform with a K40 GPU shows a speedup of 56 and 107 over sequential implementations for 2D and 3D respectively