基于信念传播的扩散张量成像中白质纤维束分割
Belief propagation based segmentation of white matter tracts in DTI.
作者信息
Bazin Pierre-Louis, Bogovic John, Reich Daniel, Prince Jerry L, Pham Dzung L
机构信息
Johns Hopkins University, Baltimore, USA.
出版信息
Med Image Comput Comput Assist Interv. 2009;12(Pt 1):943-50. doi: 10.1007/978-3-642-04268-3_116.
Abstract
This paper presents a belief propagation approach to the segmentation of the major white matter tracts in diffusion tensor images of the human brain. Unlike tractography methods that sample multiple fibers to be bundled together, we define a Markov field directly on the diffusion tensors to separate the main fiber tracts at the voxel level. A prior model of shape and direction guides a full segmentation of the brain into known fiber tracts; additional, unspecified fibers; and isotropic regions. The method is evaluated on various data sets from an atlasing project, healthy subjects, and multiple sclerosis patients.
摘要
本文提出了一种用于分割人脑扩散张量图像中主要白质束的置信传播方法。与通过对多条纤维进行采样以捆绑在一起的纤维束成像方法不同，我们直接在扩散张量上定义一个马尔可夫场，以便在体素级别分离主要纤维束。形状和方向的先验模型指导将大脑完整分割为已知纤维束、其他未指定纤维以及各向同性区域。该方法在来自图谱项目、健康受试者和多发性硬化症患者的各种数据集上进行了评估。
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