LAUSR

Brain extraction is a process of removing non-brain tissue in the brain magnetic resonance (MR) images and serves as a first step towards more delicate brain segmentation. Although many brain extraction methods have been proposed in the literature, most of them are either laborious or time consuming, and lack of instant visualization. This leads to a time lag between image acquisition and comprehensive visualization. Especially for intraoperative image based neurosurgery navigation, the time lag from image acquisition to brain visualization should be reduced as much as possible. In this paper, we propose an end-to-end fast brain extraction and visualization framework. The input is a T1-weighted MR volume and the output is comprehensive brain visualization. An improved brain extraction tool (BET) algorithm is proposed to evolve a 3D active mesh model to fit the brain surface in the 3D image. Then the brain mask is generated per slice using a polygon fill algorithm. At last, a ray-casting volume rendering algorithm is used to visualize the brain surface with the help of the generated mask. All the operations are performed using the modern OpenGL pipelines running on a graphics processing unit (GPU). Experiments were performed on two publicly available datasets and one clinical dataset to compare our method with five state-of-the-art methods including the original BET in terms of segmentation accuracy and time cost. Our method achieved mean Dice coefficients of 96.8%, 97.1%, 98.5% and mean time cost of 361 ms, 341 ms, 502 ms on the three datasets, outperforming all the other methods.