Automatic Population HARDI White Matter Tract Clustering by Label Fusion of Multiple Tract Atlases.
Automatic Population HARDI White Matter Tract Clustering by Label Fusion of Multiple Tract Atlases.
复制标题
自动人口Hardi白质界通过多个道图谱的标签融合。
DOI：
10.1007/978-3-642-33530-3_12
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发表时间：
2012-01-01
期刊：
影响因子：
--
通讯作者：
Thompson PM
中科院分区：
文献类型：
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作者：
Jin Y;Shi Y;Zhan L;Li J;de Zubicaray GI;McMahon KL;Martin NG;Wright MJ;Thompson PM
在弥散加权脑MRI中自动标记白质纤维对于比较不同人群的大脑完整性和连通性至关重要，但具有挑战性。全脑神经束造影在整个大脑中产生了大量的纤维，但由于白质通路的轨迹和形状存在很大的个体差异，因此很难将它们聚集成具有解剖学意义的神经束。我们提出了一种新的纤维束自动标记算法，该算法融合了来自纤维束成像和多个手工标记的纤维束地图集的信息。由于流线神经束造影可以产生大量假阳性纤维，我们开发了一种自上而下的方法来提取与已知解剖结构一致的神经束，基于多个手工标记的地图集的距离度量。采用多阶段融合方案，对不同地图集的聚类结果进行融合。我们的“标签融合”方法可靠地从100名年轻正常成年人的105梯度HARDI扫描中提取主要束。
Automatic labeling of white matter fibres in diffusion-weighted brain MRI is vital for comparing brain integrity and connectivity across populations, but is challenging. Whole brain tractography generates a vast set of fibres throughout the brain, but it is hard to cluster them into anatomically meaningful tracts, due to wide individual variations in the trajectory and shape of white matter pathways. We propose a novel automatic tract labeling algorithm that fuses information from tractography and multiple hand-labeled fibre tract atlases. As streamline tractography can generate a large number of false positive fibres, we developed a top-down approach to extract tracts consistent with known anatomy, based on a distance metric to multiple hand-labeled atlases. Clustering results from different atlases were fused, using a multi-stage fusion scheme. Our “label fusion” method reliably extracted the major tracts from 105-gradient HARDI scans of 100 young normal adults.