Hippocampus MRI Parallel Segmentation Using Three Dimensions Lattice Boltzmann Model with Prior Information

Getting volume change of hippocampus by segmenting on brain MRI is an important step in the diagnose of Alzheimer's disease and other brain disease. Three dimensional segmentation can make use of the correlation of image in gray and spatial position, so it has high accuracy. This paper proposes a novel three-dimensional lattice Boltzmann model combined with the surface evolution of deformable model and taking the prior information as an external force term to constrain the evolution of three dimensional surfaces. In order to solve the problem of high computational cost caused by 3D segmentation, the parallelization of the method is programmed on single GPU platform and dual GPU platform. Comparison experiments were set to test the accuracy of segmentation and computational efficiency between the novel LB method and another method by using 20 real AD patient's MRI from ADNI. In ensuring the accuracy of the segmentation, the time can be reduced to 12.76 s on single GPU platform, and 17.32 s on dual GPU platform, contrasting 132.43 s on CPU platform. It fully validates the characteristics of lattice Boltzmann method which can be highly parallelized.