Partitioning and mapping a fast level-set algorithm on the GPU

Level-set methods are commonly used to segment regions of interest within images or volumes. These tasks usually involve a high number of operations. GPUs nowadays feature high computation and data throughput capabilities. In this work we present two GPU implementations of the level-set-based segmentation method called Fast Two Cycle. Our solutions partition the computational domain in tiles that can be processed in parallel. The original algorithm is adapted to the special features of the GPU, and performance is optimized by keeping a record of the tiles that require processing at any given time. We have tested our implementations with a set of 3D CT images of brain vessels and we show that we can obtain competitive results using commodity hardware.