摘要
在脑部磁共振图像中分割海马体,快速准确地获得其体积变化情况,对于阿尔茨海默症等疾病的诊断具有重要意义。三维分割可利用图像在灰度和空间位置上的相关性,因此具有较高的准确率。该文提出了一种利用三维格子玻尔兹曼模型,结合形变模型曲面演化思想,以先验信息作为外力项,约束三维曲面演化的方法。为解决三维分割由于演化曲面复杂所带来的计算代价高的问题,分别在单GPU平台和双GPU平台上实现了方法的并行计算。为验证该文方法的准确性与效率,对20组采自ADNI数据库的阿尔茨海默症患者脑部磁共振图像进行分割实验。在保证分割精度的前提下,将原来需要132.43 s完成的分割,在单GPU平台上缩减至12.76 s,在双GPU平台上缩减至17.32 s,充分验证了格子玻尔兹曼方法可高度并行化的特点。
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 3 D 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.
出处
《中国医疗器械杂志》
2018年第1期1-6,13,共7页
Chinese Journal of Medical Instrumentation
基金
国家自然科学基金(61675124
61171146)
上海市科委科技支撑计划(15441905400)
