Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas
Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas
复制标题
DOI：
10.1016/j.neuroimage.2012.02.071
复制
发表时间：
2012-07-16
期刊：
影响因子：
5.7
通讯作者：
Mangin, J. -F.
中科院分区：
文献类型：
--
作者：
Guevara, P.;Duclap, D.;Mangin, J. -F.
本文提出了一种从大规模dMRI纤维束成像数据集中自动分割白色纤维束的方法。该方法是基于一个多学科束图集来自两个级别的主题内和主题间的聚类策略。该图谱是一组受试者的大脑白色组织的模型，由一组可以在大多数人群中检测到的通用纤维束组成。每个图谱束对应于几个受试者间聚类，这些聚类被手动标记以说明潜在通路的细分，这些细分通常在受试者之间呈现较大的变异性。图集束由所有受试者内聚类的质心的多受试者列表表示，以便获得形状和定位可变性的良好采样。该图谱由每个半球的36个已知深层白色物质束和47个表层白色物质束组成，是从包含12个大脑的第一个数据库中推断出来的。它被成功地用于分割深白色物质束在第二个数据库的20个大脑和大多数的表面白色物质束在同一数据库的10个主题。(C)2012 Elsevier Inc. All rights reserved.
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. (C) 2012 Elsevier Inc. All rights reserved.