I(2)BM, CEA, Gif-sur-Yvette, France.
Neuroimage. 2012 Jul 16;61(4):1083-99. doi: 10.1016/j.neuroimage.2012.02.071. Epub 2012 Mar 5.
This paper presents a method for automatic segmentation of white matter fiber bundles from massive dMRI tractography datasets. The method is based on a multi-subject bundle atlas derived from a two-level intra-subject and inter-subject clustering strategy. This atlas is a model of the brain white matter organization, computed for a group of subjects, made up of a set of generic fiber bundles that can be detected in most of the population. Each atlas bundle corresponds to several inter-subject clusters manually labeled to account for subdivisions of the underlying pathways often presenting large variability across subjects. An atlas bundle is represented by the multi-subject list of the centroids of all intra-subject clusters in order to get a good sampling of the shape and localization variability. The atlas, composed of 36 known deep white matter bundles and 47 superficial white matter bundles in each hemisphere, was inferred from a first database of 12 brains. It was successfully used to segment the deep white matter bundles in a second database of 20 brains and most of the superficial white matter bundles in 10 subjects of the same database.
本文提出了一种从大量弥散磁共振成像（dMRI）纤维束追踪数据集自动分割白质纤维束的方法。该方法基于从两级同主体和跨主体聚类策略得出的多主体束图谱。该图谱是大脑白质组织的模型，针对一组被试计算得出，包含了一组在大多数人群中可检测到的通用纤维束。每个图谱束对应于几个跨主体集群，这些集群手动标记，以解释通常在不同被试之间存在较大变异性的潜在通路的细分。图谱束通过多主体列表表示所有同主体集群的质心，以很好地抽样形状和定位变异性。该图谱由每个半球的 36 个已知深部白质束和 47 个表浅白质束组成，是从第一个包含 12 个大脑的数据库中推断得出的。它成功地用于分割了第二个包含 20 个大脑的数据库中的深部白质束，以及同一个数据库中 10 个被试的大多数表浅白质束。
